System comprising a sensing unit and a device for processing data relating to disturbances that may occur during the sleep of a subject

ABSTRACT

The present invention relates to devices, systems and methods for detecting disturbances that may occur during the sleep of a subject.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/598,184, filed Sep. 24, 2021, which is a U.S. National Phase under 35U.S.C. § 371 of International Patent Application Serial No.PCT/EP2020/058822, filed Mar. 27, 2020, which claims the benefit ofEuropean Patent Application Serial No. 19189095.3, filed Jul. 30, 2019,and Belgian Patent Application Serial No. 2019/0028, filed Mar. 28,2019, the entire contents of each of which are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates to devices, systems and methods fordetecting disturbances that may occur during the sleep of a subject.

BACKGROUND

The most common methodology issued for the assessment of sleep disordersand more specifically sleep disordered breathing is the in-labpolysomnography (PSG). This testing requires an overnight stay in adedicated facility supervised by trained technicians. However, thismethod is expensive, time-consuming and is unable to keep pace withdemand. Multiple physiological signals are recorded during PSG testingby different types of sensor (e.g. EEG, EMG, ECG, thermistor, pressure,video). Data from these sensors are later reviewed by a health careprofessional.

Alternative systems are considered in the art, US 2017/0265801 relatesto a bruxism detection system for detection of teeth grinding andtapping. This system includes a chin mounted accelerometer that sensesand records acceleration changes at the beginning and the end of jawclenching. Data from the accelerometer is processed to distinguishbruxism related motion from other movements of the head by comparison toaccelerometer threshold values.

US 2017/0035350 also relates to a bruxism detection system. This systemincludes two masseter mounted accelerometer, the first accelerometerbeing attached to the skin of the left masseter muscle and the secondbeing attached to the skin of the right masseter muscle. Bruxism isdetected when the recorded data of the two accelerometers issubstantially equal.

US 2007/273366 relates to a sleep disorder detector system. This systemincludes a device for measuring distances by detection of emittedmagnetic fields. The device can be mounted on a support arranged to beapplied onto the head so as to measure movements of the mouth. Data fromthe device is processed to detect sleep respiratory disorders such assnoring.

A problem with the known systems is that the movement of the head of asubject wearing the sensing unit and that of their mandible areconsidered separately from one another. The same applies to thepositions of the head and of the mandible, which are calculated from theaccelerometer measured movements. However, data from an accelerometer islimiting and can be affected by movements of other body parts, such asthe chest or trachea during breathing. Thus, the link between thesevarious movements and positions is not taken sufficiently intoconsideration to analyse sleep disturbances, which can have a negativeimpact on a diagnosis to be based on the measured data streams.

In fact, a mandibular movement may be induced by either respiratory ornon-respiratory movements. Thus, a movement of the head when the humanbeing is sleeping may cause a movement of their mandible. The mandiblemay be considered as both a mechanical linkage with the tracheal tug oran effector of the brain control. Thus, mandibular movement may bepassively induced by the breathing movements of the tracheal tug, ordirectly controlled by the brain. The tug is the traction exerted by thethorax on the head of the human being. This traction is at therespiratory frequency of that human being, because the thorax is movedwith respiration because the respiratory muscles are controlled by thebrain. Thus, if the head moves at the respiratory frequency, themandible, which is attached to the head, will follow the movementimposed by the head, at the respiratory frequency. This is a passivemovement that follows that of the head. The mandibular movement mayequally be controlled directly and actively by the brain, even when thehead may not move, or most often does not move. The brain controls themandibular movements by stimulating a group of jaw muscles. It istherefore useful to be able to make a distinction between a mandibularmovement controlled by the brain or by the attachment with tracheal tug.There is a need for a system that can more accurately interpret signalsfrom the brain and more accurately identify sleeping disorders.

SUMMARY OF THE INVENTION

An object of the invention is to provide a system of a sensing unit andof a data processing device for associating in time the measurements ofthe movements and positions of the head and of the mandible of a subjectduring the analysis of the measured data.

In particular, the present invention concerns a system (equivalently, acombination) comprising a sensing unit and a processing unit forprocessing data relating to disturbances that may occur during the sleepof a subject, which sensing unit includes gyroscope adapted to measuremovements of the mandible of a subject. The inventors have surprisinglyfound that the use of a gyroscope allows to capture mandibular spin andtherefore to assess the activity of the brainstem which controls themandibular motion during sleep.

In some embodiments, the present invention concerns a system comprisinga sensing unit and a device for processing data, e.g. processing unit,relating to disturbances that may occur during sleep of a subject, whichsensing unit includes an accelerometer adapted to measure movements ofthe head and/or of the mandible of a subject and a gyroscope adapted tomeasure movements of the mandible of that subject. The sensing unit isadapted to produce measurement signals based on the measurementseffected and the processing unit includes first and second inputs forreceiving a first, respectively a second, time stream of measurementsignals coming from the accelerometer, respectively the gyroscope.

Thus provided herein is a system for characterizing sleep disorders in asubject having a head and a mandible comprising a gyroscope, a dataanalysis unit which are connected by a data link. In particularembodiments, the system is characterized in that it comprises:

-   -   a gyroscope configured for measuring rotational movements of the        mandible of the subject;    -   a data analysis unit and a data link, the data link being        configured for sending measured rotational movement data from        the gyroscope to the data analysis unit;        wherein the data analysis unit comprises a memory unit which is        configured for storing N mandible movement classes, wherein N is        an integer larger than one, and wherein at least one of the N        mandible movement classes is indicative of a sleep disorder        event;    -   wherein each j^(th) (1≤j≤N) mandible movement class comprises of        a j^(th) set of rotational values, each j^(th) set of rotational        values being indicative of at least one rate, rate change,        frequency, and/or amplitude of mandibular rotations associated        with the j^(th) class;    -   wherein the data analysis unit comprises a sampling element        configured for sampling the measured rotational movement data        during a sampling period, thereby obtaining sampled rotational        movement data;    -   wherein the data analysis unit is configured to derive a        plurality of measured rotational values from the sampled        rotational movement data; and,    -   wherein the data analysis unit is further configured for        matching the measured rotational values with the N mandible        movement classes.

In some embodiments, the system comprises an accelerometer that isadapted to measure accelerations, the accelerations being indicative ofmovements and/or positions of the head and/or mandible of the subject,

-   -   the data link further being configured for sending measured        acceleration data from the accelerometer to the data analysis        unit;    -   wherein each j^(th) (1≤j≤N) mandible movement class comprises of        a j^(th) set of acceleration values, each j^(th) set of        acceleration values being indicative of at least one mandibular        movement or head movement associated with the j^(th) class;    -   wherein the sampling element is configured for sampling the        measured acceleration data during a sampling period, thereby        obtaining sampled acceleration data;    -   wherein the data analysis unit is configured to derive a        plurality of measured acceleration values from the sampled        acceleration data; and,    -   wherein the data analysis unit is further configured for        matching the measured acceleration values with the N mandible        movement classes.

In some embodiments, the system further comprises a magnetometer, themagnetometer being adapted to measure magnetic field data, variations inmagnetic field data being indicative of movements of the head and/or ofthe mandible of said subject,

-   -   the data link further being configured for sending measured        magnetic field data from the accelerometer to the data analysis        unit;    -   wherein each j^(th) (1≤j≤N) mandible movement class comprises a        j^(th) set of magnetic field data values, each j^(th) set of        magnetic field data values being indicative of at least one rate        or rate change of mandibular movement or head movement        associated with the j^(th) class;    -   wherein the data analysis unit comprises a sampling element        configured for sampling the measured magnetic field data during        a sampling period, thereby obtaining sampled magnetic field        data;    -   wherein the data analysis unit is configured to derive a        plurality of measured magnetic field values from the sampled        magnetic field data; and,    -   wherein the data analysis unit is further configured for        matching the measured magnetic field values with the N mandible        movement classes.

In some embodiments, the gyroscope, and optionally the accelerometerand/or the magnetometer or a part thereof are comprised in a sensingunit, the sensing unit being mountable on the mandible of the subject.

In some embodiments, one or more of the N mandible movement classes arecharacterized by a predetermined frequency range.

In some embodiments, the analysis unit is configured for identifying amovement of the head of the subject based on the gyroscope data, on theaccelerometer data, and/or the magnetometer data.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being awake, and a plurality of the N mandiblemovement classes is indicative of the subject being asleep.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being in an N1 sleeping state; and wherein atleast one of the N mandible movement classes is indicative of thesubject being in a REM sleeping state; optionally wherein at least oneof the N mandible movement classes is indicative of the subject being inan N2 sleeping state and/or wherein at least one of the N mandiblemovement classes is indicative of the subject being in an N3 sleepingstate.

In some embodiments, one or more of the N mandible movement classes areindicative of an obstructive apnoea, an obstructive hypopnoea, arespiratory effort linked to arousal, a central apnoea, and/or a centralhypopnoea.

In some embodiments, one of the N mandible movement classes isindicative of bruxism, and wherein the measured rotational movement datais indicative of a mandibular movement amplitude of at least 1 mm, at afrequency established in a range of 0.5 to 5 Hz during at least threerespiratory cycles when the movement is phasic, or beyond 1 mm in asustained, tonic manner for at least 2 seconds.

Further provided is a method for assisting in the characterization ofsleep disorders in a subject having a mandible, comprising the steps:

-   -   receiving, by a data analysis unit and via a data link,        rotational movement data from a gyroscope positioned on the        mandible of the subject;    -   storing, by means of a memory unit comprised in the data        analysis unit, N mandible movement classes, wherein N is an        integer larger than one, and wherein at least one of the N        mandible movement classes is indicative of a sleep disorder        event;    -   wherein each j^(th) (1≤j≤N) mandible movement class consists of        a j^(th) set of rotational values, each j^(th) set of rotational        values being indicative of at least one rate, rate change,        frequency, or amplitude of mandibular rotations associated with        the j^(th) class;    -   sampling, by means of a sampling element comprised in the data        analysis unit, the rotational movement data during a sampling        period, thereby obtaining sampled rotational movement data;    -   deriving, by means of the data analysis unit, a plurality of        measured rotational values from the sampled rotational movement        data; and,    -   matching, by means of the data analysis unit, the measured        rotational values to the N mandible movement classes.

In some embodiments, the method further comprises the steps of:

-   -   measuring accelerations by means of an accelerometer, the        accelerations being indicative of movements and/or positions of        the head and/or mandible of the subject;    -   sending, by means of the data link, measured acceleration date        from the accelerometer to the data analysis unit;    -   wherein each j^(th) (1≤j≤N) mandible movement class comprises of        a j^(th) set of acceleration values, each j^(th) set of        acceleration values being indicative of at least one mandibular        movement or head movement associated with the j^(th) class;    -   sampling, by means of a sampling element, the measured        acceleration data during a sampling period, thereby obtaining        sampled acceleration data;    -   deriving, by means of the data analysis unit, a plurality of        measured acceleration values from the sampled acceleration data;        and,    -   matching, by means of the data analysis unit, the measured        acceleration values with the N mandible movement classes.

In some embodiments, the method further comprises the steps of:

-   -   measuring, by means of a magnetometer, magnetic field data, the        variations in magnetic field data being indicative of movements        of the head and/or of the mandible of said subject;    -   sending, by means of the data link, measured magnetic field data        from the magnetometer to the data analysis unit;    -   wherein each j^(th) (1≤j≤N) mandible movement class comprises of        a j^(th) set of magnetic field data values, each j^(th) set of        magnetic field data values being indicative of at least one rate        or rate change of mandibular movement or head movement        associated with the j^(th) class;    -   sampling, by means of a sampling element comprised in the data        analysis unit, the measured magnetic field data during a        sampling period, thereby obtaining sampled magnetic field data;    -   deriving, by means of the data anlaysis unit, a plurality of        measured magnetic field values from the sampled magnetic field        data; and,    -   matching, by means of the data analysis unit, the measured        magnetic field values with the N mandible movement classes.

In some embodiments, the method further comprises the step ofidentifying, by means of the analysis unit, a movement of the head ofthe subject based on the gyroscope data, on the accelerometer data,and/or the magnetometer data.

In some embodiments, at least one of the N mandible movement classes isindicative of bruxism, and wherein the measured rotational movement datais indicative of a mandibular movement amplitude of at least 1 mm, at afrequency established in a range of 0.5 to 5 Hz during at least threerespiratory cycles when the movement is phasic, or beyond 1 mm in asustained, tonic manner for at least 2 seconds.

DESCRIPTION OF THE FIGURES

The invention will now be described in more detail with the aid of thedrawings, which show the system and its operation. The present systemmay be described as a system of a sensing unit and a device or unit forprocessing sensed data. In the drawings:

FIG. 1 shows a system according to the invention.

FIGS. 2A and 2B show two streams during a change in the position of thehead of a human being lying in bed.

FIGS. 3A and 3B show streams captured by the sensing unit duringbruxism.

FIG. 4 shows the loop gain.

FIG. 5 shows the identification of micro-arousals followingpreprocessing.

FIG. 6 shows the measured signal after application of band-passfiltering.

FIG. 7 shows a signal indicating micro-arousals.

FIG. 8 shows an example of the first and second measurement signalstreams in the case of obstructive apnoea;

FIG. 9 shows an example of the first and second measurement signalstreams in the case of obstructive hypopnoea;

FIG. 10 shows an example of the first and second measurement signalstreams in the case of mixed apnoea;

FIG. 11 shows an example of the first and second measurement signalstreams in the case of central apnoea;

FIG. 12 shows an example of the first and second measurement signalstreams in the case of central hypopnoea;

FIG. 13 shows an example of the first and third measurement signalstreams during respiratory-effort related arousal (RERA); and

FIG. 14 shows spectrograms of the frequency distribution of themandibular movement.

FIG. 15 shows an exemplary procedure for feature extraction, dataprocessing, and data description.

FIGS. 16 and 17 show an analysis of mandibular movement data captured bymeans of a magnetic sensor.

FIG. 18 shows an exemplary method for automated sleep stages detectionfrom mandibular movement data captured by means of gyroscope and anaccelerometer. The method is discussed further in Example 18.

In FIG. 1 , the following numbering is used: 1—sensing unit;2—accelerometer; 3—gyroscope; 4—magnetometer; 5—oximeter; 6—thermometer;7—audio sensor; 8—electromyography unit; 9—pulse photoplethysmograph;10—device for processing data; 11-1—first input; 11-2—second input;11-3—third input; 11-4—fourth input; 12—identifying unit; 13—analysisunit.

DETAILED DESCRIPTION

Before the present systems and processes of the invention are described,it is to be understood that this is not limited to particular systemsand methods or combinations described, since such systems and methodsand combinations may, of course, vary. It is also to be understood thatthe terminology used herein is not intended to be limiting, since thescope will be limited only by the appended claims.

As used herein, the singular forms “a”, “an”, and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise.

The terms “comprising”, “comprises” and “comprised of” as used hereinare synonymous with “including”, “includes” or “containing”, “contains”,and are inclusive or open-ended and do not exclude additional,non-recited members, elements or method steps. It will be appreciatedthat the terms “comprising”, “comprises” and “comprised of” as usedherein comprise the terms “consisting of”, “consists” and “consists of”.

The recitation of numerical ranges by endpoints includes all numbers andfractions subsumed within the respective ranges, as well as the recitedendpoints.

The term “about” or “approximately” as used herein when referring to ameasurable value such as a parameter, an amount, a temporal duration,and the like, is meant to encompass variations of +/−10% or less,preferably +/−5% or less, more preferably +/−1% or less, and still morepreferably +/−0.1% or less of and from the specified value, insofar suchvariations are appropriate to perform in the disclosed aspects andembodiments. It is to be understood that the value to which the modifier“about” or “approximately” refers is itself also specifically, andpreferably, disclosed.

Whereas the terms “one or more” or “at least one”, such as one or moreor at least one member(s) of a group of members, is clear per se, bymeans of further exemplification, the term encompasses inter alia areference to any one of the members, or to any two or more of themembers, such as, e.g., any ≥3, ≥4, ≥5, ≥6 or ≥7 etc. of the members,and up to all the members.

All references cited in the present specification are herebyincorporated by reference in their entirety. In particular, theteachings of all references herein specifically referred to areincorporated by reference.

Unless otherwise defined, all terms used herein, including technical andscientific terms, have the meaning as commonly understood by one ofordinary skill in the art. By means of further guidance, termdefinitions are included to better appreciate the teaching as describedherein.

In the following passages, different aspects are defined in more detail.Each aspect so defined may be combined with any other aspect or aspectsunless clearly indicated to the contrary. In particular, any featureindicated as being preferred, particular or advantageous may be combinedwith any other feature or features indicated as being preferred,particular or advantageous.

The present invention relates to the measurement and assessment ofmandibular movement of a sleeping subject. The mandible or lower jawbonesits beneath the maxilla and forms the lower jaw. It is the only movablebone of a human skull (discounting the ossicles of the middle ear).During movement, the mandible pivots around the temporomandibular joint,where the mandible connects to the skull (temporal bone) in front of theear. During mandibular movement the relationship between the length andthe tension of muscular fibres anchored on the mandible will change,which may result in a stiffening of the upper airways in subjects whoare at risk of instability during sleep. This movement is activatedunder agonist and antagonist muscles for elevating or lowering themandible, thereby closing or opening the mouth, respectively. Theagonist and antagonist muscles are innervated by motor neuronsoriginating from the nucleus of the trigeminal nerve located in thebrainstem (mid-pons) and are supported by the motor branch of thisnerve.

Provided herein is a system for characterizing sleep disorders in asubject having a head and a mandible. The system comprises a gyroscope.The gyroscope is configured for measuring rotational movements of themandible of the subject, which, as observed by the inventors, is anactivity that gyroscopes are particularly well-suited for. The gyroscopecan be used to assess the activity of the brainstem stimulating mandiblemovement during sleep, in a way to keep open the upper airways (pharynx)and prevent from sleep disordered breathing. The mandibular mobile boneis turned around like a lever to stretch pharyngeal muscular fibresattached directly or indirectly (via the second mobile bone—the hyoidbone) including the tongue, on the mandibular bony arch.

To some extent the gyroscopic movement is representative of the centraldrive meaning that the nucleus of the trigeminal nerve in the pons isacting to finely displace the mandible with regard to the respiratorycentres located also in the brainstem and under the influence of highercentres responsible for the sleep organization (sleep staging). As aresult, the provision of a gyroscope in a sensing unit can be used forassessing various sleep related activities, by looking at the rotationalmandibular displacements, which may include respiration, sleep stages orother events (e.g. movement or motor events). Moreover, values measuredby gyroscope arranged for measuring rotational movements of the mandibleof the subject, such as the rate and the amplitude of the mandibulargyroscopic signal, in addition to metrics directly or indirectly derivedfrom the measured values, can be used for obtaining assessment of thecentral drive stemming from the nucleus of the trigeminal nerve.

The inventors have found that other sensing units are not suitable forthe measurement and assessment of mandibular movement as providedherein. For instance, an inertial sensor like an accelerometer allowsonly a limited measurement of linear acceleration and is thus unsuitablefor measurement of rotational mandibular displacements. Measurement byan accelerometer can be affected by movement of the body or the head,such as the chest or trachea during breathing and distinguishing betweenthe origins of data is difficult and adds unnecessary noises andcomplexity to the system. As a result, the link between the possiblebody and head movement is not taken sufficiently into consideration byexisting systems for analysis of sleep disturbances. This has a negativeimpact on a diagnosis that is based on the measured data streams. Theinventors have found that the rotation of the mandible carries thenecessary information to arrive at an accurate assessment and moreoverthat such movement can accurately be recorded by a gyroscope.

The system further comprises a data analysis unit and a data link. Thedata link provides a communication path between the gyroscope and thedata analysis unit. Preferably, the data link is a wireless datalink,e.g. because of improved subject comfort, though data links employingcommunication by wire are certainly possible as well.

Rotational movement data is sent via the data link from the gyroscope tothe data analysis unit. The data link is of a conventional nature andcontains arrangements for transferring data either wirelessly or bywire.

The data analysis unit comprises a memory unit, e.g. a data storagedevice such as hard drive, solid-state drive, memory card or the like.The memory unit is configured for storing a number (N) mandible movementspecific patterns (classes), with N an integer larger than one. At leastone of the N mandible movement classes is indicative of a sleep disorderevent. Preferably, the N mandible movement classes comprise a pluralityof movement classes which are indicative of various mandibularmovements. Each j^(th) (1≤j≤N) mandible movement class comprises of aj^(th) set of rotational values, and each j^(th) set of rotationalvalues is indicative of at least one rate, rate change, frequency,and/or amplitude of mandibular rotations associated with the j^(th)class.

The rotational movement data measured or recorded by the gyroscope islinked to the mandible movement classes as follows:

The data analysis unit comprises a sampling element configured forsampling the measured rotational movement data during a sampling period.Thus, sampled rotational movement data is obtained. Informationcontained in the signals recorded by the gyroscope may thus be extractedfor further analysis. It shall be understood that in some embodiments,the data analysis unit may be comprised in a general purpose computingdevice such as a personal computer or a smartphone, though the provisionof specialized hardware is certainly possible as well.

The data analysis unit is configured to derive a plurality of measuredrotational values from the sampled rotational movement data, and formatching the measured rotational values with the N mandible movementclasses. Preferably, deriving the measured rotational values from thesampled rotational movement data comprises one or more of the followingprocedures: discretization, time-averaging, fast Fourier transformation,etc. Additionally, the matching may be fully or partially automated bythe provision of a machine learning model, such that the data analysisunit is configured to learn a number of statistical and/or physicalmetrics in order to capture the characteristics of the signal infrequency and time domains and identify patterns of rotation signal tospecific events, such as sleep stages, respiratory efforts, and thelike. The provision of a machine learning model may thus provide forautomatic interpretation of the relevant information and/or matchingcharacteristic data with sleep disorder events Study of mandibularmovement during sleep therefore provides information on the respiratorycontrol state in response to changes of permeability or of resistance toflow of the air flows in the upper respiratory tracts, whether or notthat is involved in series of modifications in the position of the head.

Analysis of the nature of mandibular movement using the system accordingto the invention can also detect non-respiratory motor events repeatedduring sleep, such as bruxism or chewing, or of an isolated nature, suchas oro-facial dyskinesia. Deglutition and suckling movements in theinfant can also be clearly identified. Additionally, deglutitionmovements can be detected in adults as well. This allows differentiatingarousals from micro-arousals.

In some embodiments, one or more mandibular movement classes areindicative of an isolated large mandibular movement (IMM). IMMs areassociated with micro-arousals or respiratory disturbance inducedarousals, such that micro-arousals can be effectively inferred from themeasurements and the analysis.

In some embodiments, the process of matching the measured rotationalvalues with the N mandible movement classes makes use of an artificialintelligence method, for example random forests.

In some embodiments, the system further comprises an accelerometer. Theaccelerator is adapted to measure accelerations (including accelerationvariations) which are indicative of movements and/or positions of thehead and/or mandible of the subject. The inventors have found thataccelerometers are particularly well-suited for measuring movements andpositions of the head. The addition of an accelerometer to the presentsystem allows for further assessing the behaviour of the mandible duringsleep. In particular, the inventors have found that the measurement ofaccelerations can be used to explain unexpected changes in the movement,amplitude and/or rate of the gyroscope. Measurements by an accelerometermay thus be used to supplement measurements performed by the gyroscope.

Measured or recorded acceleration data is sent by the accelerometer tothe data analysis unit via the data link. In these embodiments, eachj^(th) (1≤j≤N) mandible movement class comprises of a j^(th) set ofacceleration values. Each j^(th) set of acceleration values or metricsis indicative of at least one mandibular movement or head movementassociated with the j^(th) class. The sampling element is configured forsampling the measured acceleration data during a sampling period. Aftersampling, the measured acceleration data are termed sampled accelerationdata. The data analysis unit is configured to derive a plurality ofmeasured acceleration values from the sampled acceleration data, forexample by discretization and optionally time averaging. Informationcontained in the signals recorded by the accelerometer may be extractedfor further analysis. The data analysis unit is further configured formatching the measured acceleration values with the N mandible movementclasses. This process of matching is understood to involve automaticallydetermining the mandible movement class that corresponds the closest tothe measured acceleration values. The matching may be fully or partiallyautomated by the provision of a machine learning model, which mayprovide for automatic interpretation of the relevant information and/ormatching characteristic data with sleep disorder events.

The inventors have found that the accelerometer is particularlysensitive to movements of the head. Together, the gyroscope and theaccelerometer allow efficiently discerning head movements from mandiblemovements, which in turn allows for improved detection of sleep disorderevents. As a result, the provision of a gyroscope and an accelerometerin a single system can increase the sensitivity and accuracy of thepresent system, and may also for assessment of new information thatcould not be interpreted from the measured values provided by agyroscope or accelerometer alone. For example, changes in the headposition stimulated by a central activation could impact the mandiblerotation movement, which could be mistakenly interpreted as changes inthe degree of mouth opening or closing. The combination of a gyroscopeand an accelerometer can thus allow for discerning head movement fromjaw movement. In view of the superior and unexpected functionalityprovided by the present combination, the presence of a gyroscope cannotbe regarded an alternative to other sensing devices, like for example asecond accelerometer.

In some embodiments, the system further comprises a magnetometer, themagnetometer adapted to measure magnetic field data. The variations inmagnetic field data are indicative of the direction of movements and/orpositions of the head and/or of the mandible of said subject. Theaddition of a magnetometer to the present system may allow for furtherassessing the behaviour of the mandible during sleep. It may beappreciated that the provision of a magnetometer in the present systemserves to assess the orientation of the sensing unit similar to acompass. As such, the magnetometer is not intended to serve as a unitfor measuring distances as contemplated in systems of the art; althoughthe primary functionality is understood to be not limitative to thescope of the present system.

The data link is further configured for sending measured or recordedmagnetic field data from the magnetometer to the data analysis unit.Each j^(th) (1≤j≤N) mandible movement class comprises a j^(th) set ofmagnetic field data values. Each j^(th) set of magnetic field datavalues is indicative of at least one rate or rate change of mandibularmovement or head movement associated with the j^(th) class. The dataanalysis unit comprises a sampling element configured for sampling themeasured magnetic field data during a sampling period. Thus sampledmagnetic field data is obtained. The data analysis unit is configured toderive a plurality of measured magnetic field values from the sampledmagnetic field data. The data analysis unit is further configured formatching the measured magnetic field values with the N mandible movementclasses.

In a particular form, the magnetometer may comprise two parts: one partmounted on the forehead of a patient, and one part mounted on themandible of the patient. The inventors have found that this is aparticularly effective configuration for detecting mandibular movements.

In some embodiments, signals originating from the magnetometer,gyroscope, accelerometer and/or further sensors are transferred via asingle physical medium using, for example time-division multiplexingand/or using carrier waves of different frequencies.

In some embodiments, the gyroscope, and/or the accelerometer, and/or themagnetometer or a part thereof are comprised in a sensing unit. Thesensing unit is mountable on the mandible of the subject. This is anembodiment with a highly compact form factor, it is easy to apply, andoffers improved patient comfort. The provision of an accelerometerand/or magnetometer is understood to not substitute the functionality ofa gyroscope, but rather to arrive at new interpretations that are madepossible only through the combination of gyroscope with one ore moreadditional sensing devices, such as an accelerometer and/or themagnetometer. Preferably, interpretation of the acquired signal isfirstly associated with data from the gyroscope and in a second stepsupplemented with data from the accelerometer and/or magnetometer. Forexample, data from the gyroscope may be used first for analysis of theangular speed of the mandible to arrive at a comprehensive cycle bycycle analysis; data from the accelerometer may then be used to providecontext on which cycle is produced (e.g, the origin of the activation(cortical and subcortical), the endotype (the dynamic of the breathingdisorder), the types of the muscular masticatory activity (more or lesstonic or phasic)). Additionally, novel assessment can be made from thecombination of data that are not possible based on data from a singlesensing unit alone. For example, precise description of the event typeopens the possibility of making predictions about the occurrence orreoccurrence of sleep disorder event or changes in breathing (e.g.peripheral capillary oxygen saturation SpO₂).

Preferably, the sensing unit has a size of at most 5 cm long, 2 cm thickand 1 cm high. This reduces interference with the normal sleep of thesubject.

In some embodiments, one or more of the N mandible movement classes areassociated with a predetermined frequency range. In other words, inthese embodiments one or more of the N mandible movement classescomprise mandible movements which occur in a pre-determined frequencyrange. Preferably, at least two of the N mandible movement classes areassociated with a predetermined frequency range, including an A^(th)predetermined frequency range and a B^(th) predetermined frequencyrange, and the A^(th) predetermined frequency range and thepredetermined frequency range do not overlap.

In some embodiments, at least one pre-determined frequency rangeconsists of frequencies between 0.15 Hz to 0.60 Hz, or between 0.25 Hzand 0.50 Hz, or between 0.30 Hz and 0.40 Hz. This is the frequency rangeof signals which are indicative of breathing of the subject.

In some embodiments, the system further comprises one or more ancillarycomponents selected from the list comprising an oximeter and/or athermometer and/or an audio sensor and/or an electromyography unitand/or a pulse photoplethysmograph. Preferably, these ancillarycomponents are operationally connected to the analysis unit via a datalink.

In some embodiments, the analysis unit is configured for identifying amovement of the head of the subject based on the gyroscope data, and/orthe accelerometer data, and/or the magnetometer data. Preferably, themovement of the head comprises a rotation, e.g. a rotation around anaxis through the centre of the head of the subject. Preferably then, atleast one of the N mandible movement classes is indicative of a changeof position of the head. This allows efficiently discerning generic headmovements from movements of the mandible per se. In these embodiments,the system preferably comprises both an accelerometer and a gyroscope.

In some embodiments, the analysis unit is adapted to apply one or morepre-processing steps to the gyroscope data, and/or the accelerometerdata, and/or the magnetometer data. The one or more pre-processing stepsare selected from the list comprising: the application of a band passfilter, the application of a low pass filter, an exponential mobilemean, and/or a calculation of the entropy of the frequency of thegyroscope data, and/or the accelerometer data, and/or the magnetometerdata. The application of low pass filtering improves the detection ofmicro-arousals.

In some embodiments, the analysis unit may comprise an interpretationmodule configured for interpreting specific parameters which measure thesleep quality and the extend of sleep breathing disturbances. The sleepquality parameters may include, e.g., total sleep time (TST), seep onsetlatency (SOL), first awake from sleep onset (WASO), awake index, sleepefficiency (SE), ratios of REM, nonREM sleep, REM sleep latency, andother sleep quality metrics. The sleep respiratory disturbances relatedmetrics may include the hourly occurring rate and cumulated duration ofrespiratory efforts during sleep.

The analysis unit may be configured for reporting the interpretedsubject specific parameters. The reporting may include providing anoutput to a device, such as a computer or smartphone. The reporting mayalso include providing a visual or textual report of the subjectspecific parameters, for example in the form of a hypnogram.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being awake, and wherein a plurality of the Nmandible movement classes is indicative of the subject being asleep.Incorporating a classification of “asleep” and “awake” in the presentmethods ensures that measurements done while the subject is in awake orasleep are interpreted accordingly. The interpretation may be performedusing an interpretation module.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being in an N1 sleeping state; and at leastone of the N mandible movement classes is indicative of the subjectbeing in a REM sleeping state. Optionally, at least one of the Nmandible movement classes is indicative of the subject being in an N2sleeping state and/or at least one of the N mandible movement classes isindicative of the subject being in an N3 sleeping state.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being in an N2 sleeping state.

In some embodiments, at least one of the N mandible movement classes isindicative of the subject being in an N3 sleeping state.

In some embodiments one or more of the N mandible movement classes areassociated with a detection of a sleeping stage. Detection of sleepingstages may further be implemented for establishing a subject specificsleeping pattern. The sleeping stage detection is preferably automatedat different levels of resolution.

In preferred embodiments the sleeping patterns may include (sorted byincreasing level of complexity):

-   -   (1) 2 Class (i.e. binary) scoring for detecting the awake or        sleeping state in a subject;    -   (2) 3 Class scoring for classifying the sleeping stage,        including the awake state, nonREM sleeping stage or REM sleeping        stage in a subject;    -   (3) 4 Class scoring for classifying the sleeping stage,        including the awake state, light sleeping (N1 and N2) stage,        deep sleeping (N3) stage or REM sleeping stage in a subject;    -   (4) 5 Class scoring for classifying all sleeping stages,        including the awake state, N1 sleeping stage, N2 sleeping stage,        N3 sleeping stage and REM sleeping stage in a subject.

Exemplary method for achieving an automated sleeping stage detection of3 class scoring is provided in Examples 18 and 19.

In some embodiments, at least one of the N mandible movement classes isindicative of cortical activity.

In some embodiments, at least one of the N mandible movement classes isindicative of sub-cortical activity.

In some embodiments, one or more of the N mandible movement classes areindicative of an obstructive apnoea, an obstructive hypopnoea, arespiratory effort linked to arousal, a central apnoea, and/or a centralhypopnoea.

In some embodiments, one of the N mandible movement classes isindicative of bruxism, and the measured rotational movement data isindicative of a mandibular movement amplitude of at least 1 mm, at afrequency established in a range of 0.5 to 5 Hz during at least threerespiratory cycles when the movement is phasic, or beyond 1 mm in asustained, tonic manner for at least 2 seconds.

Bruxism during sleep is a frequent complaint by 5 to 10% of the adultpopulation. It is often intermittent, variable in time, sometimes liableto disappear for a few weeks before bouncing back and imposing itselfrepeatedly during the night, several nights in a row. Bruxism is oftenrecognized by the partner of the sleeper in the form of disagreeable andloud grinding of the teeth. This can lead to facial or temporal pain andsigns of wear of the dental enamel in the subject. Its origin is notwell understood, but the syndrome of obstructive sleep apnoea has beenreferred to as one possible cause.

In some embodiments, one or more of the N mandible movement classes isindicative of the loop gain, of the muscular gain mobilizing themandible during apnoea or hypopnoea ora period of effort, of the pointof passive collapsibility after activation and/or of the point ofarousability before activation.

Further provided herein is a method for assisting in thecharacterization of sleep disorders, for example sleep disorderedbreathing (SDB), in a subject having a mandible. The method comprisesthe following steps:

-   -   receiving, by a data analysis unit and via a data link,        rotational movement data from a gyroscope positioned on the        mandible of the subject.    -   storing, by means of a memory unit comprised in the data        analysis unit, N mandible movement classes. Note that N is an        integer larger than one, and that at least one of the N mandible        movement classes is indicative of a sleep disorder event (for        example a sleep disordered breathing (SDB) event). Each j^(th)        (1≤j≤N) mandible movement class consists of a j^(th) set of        rotational values, and each j^(th) set of rotational values is        indicative of at least one rate, rate change, frequency, or        amplitude of mandibular rotations associated with the j^(th)        class.    -   sampling, by means of a sampling element comprised in the data        analysis unit, the rotational movement data during a sampling        period. Thus sampled rotational movement data is obtained.    -   deriving, by means of the data analysis unit, a plurality of        measured rotational values from the sampled rotational movement        data; and,    -   matching, by means of the data analysis unit, the measured        rotational values to the N mandible movement classes.

Thus sleep disorders can be efficiently detected with excellent patientcomfort.

In some embodiments, the method further comprises the steps of:

-   -   measuring accelerations by means of an accelerometer. The        accelerations are indicative of movements and/or positions of        the head and/or the mandible of the subject;    -   sending, by means of the data link, measured acceleration date        from the accelerometer to the data analysis unit;    -   sampling, by means of a sampling element, the measured        acceleration data during a sampling period, thereby obtaining        sampled acceleration data;    -   deriving, by means of the data analysis unit, a plurality of        measured acceleration values from the sampled acceleration data;    -   matching, by means of the data analysis unit, the measured        acceleration values with the N mandible movement classes. Note        that in these embodiments, each j^(th) (1≤j≤N) mandible movement        class comprises of a j^(th) set of acceleration values, each        j^(th) set of acceleration values being indicative of at least        one mandibular movement or head movement associated with the        j^(th) class.

The use of both accelerometer and gyroscope allows effectivelydiscerning mandible movements from movements of the entire head.

In some embodiments, the method further comprises the steps of:

-   -   measuring, by means of a magnetometer, magnetic field data, the        variations in magnetic field data being indicative of movements        and/or positions of the head and/or of the mandible of said        subject;    -   sending, by means of the data link, measured magnetic field data        from the accelerometer to the data analysis unit;    -   sampling, by means of a sampling element comprised in the data        analysis unit, the measured magnetic field data during a        sampling period, thereby obtaining sampled magnetic field data;    -   deriving, by means of the data analysis unit, a plurality of        measured magnetic field values from the sampled magnetic field        data; and,    -   matching, by means of the data analysis unit, the measured        magnetic field values with the N mandible movement classes. Note        that in these embodiments, each j^(th) (1≤j≤N) mandible movement        class comprises of a j^(th) set of magnetic field data values,        each j^(th) set of magnetic field data values being indicative        of at least one rate or rate change of mandibular movement or        head movement associated with the j^(th) class.

In some embodiments, the method further comprises the step ofidentifying, by means of the analysis unit, a movement of the head ofthe subject based on the gyroscope data, and/or on the accelerometerdata, and/or the magnetometer data.

In some embodiments, at least one of the N mandible movement classes isindicative of bruxism, and the measured rotational movement data isindicative of a mandibular movement amplitude of at least 1 mm, at afrequency established in a range of 0.5 to 5 Hz during at least threerespiratory cycles when the movement is phasic, or beyond 1 mm in asustained, tonic manner for at least 2 seconds. This combination ofparameters is indicative of a bruxism, such that bruxism can beeffectively detected. In some embodiments, this frequency range isbetween 1.0 to 4.5 Hz, or 1.5 to 4.0 Hz, or 2.0 to 3.5 Hz, or 2.5 to 3.0Hz.

In the following, specific embodiments of matching data (e.g. preferablysampled rotational, acceleration data, and/or magnetic field data), withthe N mandible movement classes are discussed. These embodiments involvethe extraction of features from the aforementioned data. The featurescomprise measured rotational values, and optionally include measuredacceleration values, and/or measured magnetic field values. Once thefeatures are extracted, they are matched with one or more mandiblemovement classes. Preferably, the mandible movement classes that thefeatures are matched with comprise central hypopneas, normal sleep, andobstructive hypopneas. Preferably, features are matched with themandible movement classes by means of a SHAP score to interpret andexplain the matching.

In some embodiments, the features are chosen from the non-exhaustivelist comprising: central tendency (mean, median and mode) of MM (i.e.mandibular movement, signifying rotations, accelerations, and/orpositions measured using a gyroscope, accelerometer, and/ormagnetometer) amplitudes; MM distribution (raw or enveloped signals):skewness, Kurtosis, IQR, 25th, 75th and 90th centiles; extreme values:Min, Max, 5th and 95th centiles of MM amplitudes; tendency of variation:Linear trend and coefficients of Tensor product-based spline factors(S1, 2, 3, 4) from a generalized additive model to evaluate MM infunction of Time; duration of each event. It shall be understood thatsuch features refer to measured rotational values, measured accelerationvalues, and/or magnetic values, whether sampled and/or discretized ornot. Preferably, the aforementioned values are sampled and discretized.It shall be understood that the list present exemplary embodiments whichare therefore regarded as non-limiting to the present system.

In some embodiments, the extraction of features comprises isolatingevents. An event is a sequence of mandibular movement data (preferablysampled rotational, acceleration, and/or magnetic data) that can beattributed to a single movement of the head and/or the mandible. Onespecific type of event is normal breathing, for example normal breathingfor a pre-determined amount of time. The pre-determined amount of timemay be, for example, between 2 and 20 seconds, or between 5 and 15seconds, 30 seconds or 10 seconds. The time range size may be adapted tothe intended application; for example 30 seconds may be suitable foridentifying stages of sleep, 10 seconds for sleep bruxism ormicro-awakenings, 20 seconds for respiratory events, and so on,

In some embodiments, the extraction of features follows the followingprocedure comprising steps 1 to 4:

1. Obtaining sampled mandibular movement data. The mandibular movementdata comprises sampled rotational values, and optionally sampledacceleration values and/or sampled magnetic field values. Preferably,the sampling rate is from 1.0 to 100.0 Hz, or from 2.0 to 50.0 Hz, orfrom 5.0 to 25.0 Hz, preferably 10.0 Hz. Preferably, obtained sampledmandibular movement data was obtained during a period between 10.0minutes and 12.0 hours, or during a period between 20.0 minutes and 4.0hours, or during a period between 30.0 minutes and 2.0 hours.

2. Marking timestamps of mandible movement events.

3. For each time stamp ti, perform the following steps

-   -   3.a. Check whether ti is the beginning of a mandible movement        event;    -   3.b. If ti is the beginning of a mandible movement event,        -   assign ti to t_begin, and subsequently search for the ending            (t_end) of the mandible movement event; and,        -   index t_begin and t_end;

4. For each mandible movement event E, perform the following step

-   -   4.a. Calculate event duration dt=(t_end−t_begin)    -   4.b. Determine the statistical distribution of the sampled        mandibular movement data during the event. Preferably, this        involves calculating one or more features selected from the list        comprising Min, Max, Mean, median, mode, 5^(th), 25^(th),        75^(th), 90^(th), 95^(th) centiles, Skewness, Kurtosis, IQR;

Additionally or alternatively, a GAM (General Additive Model) non-linearmodel is used to estimate MM amplitude and/or position by a splinefunction on time t, then the coefficient of spline function isextracted.

Additionally or alternatively, a simple linear model is fitted, andintercept and slope are extracted from the mandibular movements,including amplitude and/or position.

Optionally, all features are concatenated.

The mandible movement event is then matched with a mandible movementclass.

In some embodiments, matching a mandible movement event with a mandiblemovement class involves the use of exploratory data visualization,one-way ANOVA, and pairwise student-t tests with Bonferroni correction.Preferably, during this procedure, significance levels are set atp=0.0001 to 0.01, more preferably at p=0.001.

In some embodiments, a machine learning method, e.g. extreme gradientboosting, deep neural network, convolutional neural network, randomforest, is used to classify the measured mandible movement data intomandible movement classes.

In some embodiments, the employed random forest method algorithm employsbetween 20 and 5000, or between 100 and 2000, or between 200 and 1000,or 500 decision trees. In some embodiments, each decision tree isconstructed on a random subset of the aforementioned features.

In some embodiments, model development (i.e. training the artificialintelligence method) involves randomly splitting the measured mandiblemovement data into two subsets, a larger set for model development and asmaller set for model validation. In some embodiments, the larger setcomprises 60 to 80%, or 70% of the measured mandible movement data. Insome embodiments, the smaller set comprises 20 to 40%, or 30% of themandible movement data. Preferably, a synthetic minority over-samplingtechnique (SMOTE), is used on the training set before the model isdeveloped.

In some embodiments, the model development involves the evaluation ofthe contribution of a plurality of features to classification by meansof the Lundberg's Shapley additive explanation (SHAP) method. The SHAPmethod thus allows for interpreting the prediction made by the employedmachine learning model; it allows for the model to be explainable.

Certain aspects of the present disclosure may be alternatively oradditionally worded as follows:

In some embodiments, the system comprises a sensing unit and a devicefor processing data relating to disturbances that may occur during thesleep of a subject. The processing device includes an identifying unitadapted to identify in the first and second measurement signal streamsfirst signals the frequency of which is situated in a firstpredetermined frequency range and second signals in which the value ofat least one intrinsic characteristic characterizing a movement of thehead and/or of the mandible is situated in a second predetermined rangeconsisting of values, said first predetermined frequency range and saidsecond predetermined range consisting of values being frequencies,respectively values, of movements of the head and of the mandible ofsaid subject that characterize a sleeping state of said subject, saididentifying unit being adapted to produce a triggering signal afterobserving that the first and second signals that have been identified inthe first and second streams are present for a first predetermined timeperiod, said identifying unit being also adapted, after it has producedthe triggering signal, to identify in the first and second measurementsignal streams third signals in which the frequency and/or the value ofsaid at least one intrinsic characteristic represents a movement of themandible and/or a change of the position of the head of said subject,said identifying unit being connected to an analysis unit adapted to beactivated under the control of the triggering signal, said analysis unitbeing also adapted to compare the third signals to profiles thatcharacterize frequencies and/or values linked to sleep disturbances andto produce a result of that comparison. The invention is based on theconcept that during sleep of the subject the respiratory movement ofthat subject is controlled by the nerve centres of the brain of thatsubject, which nerve centres control the muscles of the head and of themandible that are attached thereto, which muscles will then position thehead and the mandible of that subject. The accelerometer, as well as thegyroscope, will each supply a respective time stream of measurementsignals that characterize the movements of the head and of the mandible.Using the identifying unit makes it possible to identify in thesestreams of measurement signals those that characterize a sleeping stateof that subject and thus to activate the analysis unit to analyze anydisturbances of sleep affecting the subject when the subject is actuallyasleep.

Thus it has been found that the movement of the mandible is determined,not only by the movement of the thorax, but also directly by the nervecentres of the brain that control the muscles attached thereto and thatwill position the mandible. They also control the position of the head.

In fact the tracheal tug, which is necessarily at the respirationfrequency, can cause the head to move and it is for this reason that ameasurement by both the accelerometer and by the gyroscope is preferred.In fact, the gyroscope is more sensitive to a movement of rotation ofthe mandible actuated by its own muscles under direct control of thebrain than the accelerometer, which will show the movement of the headthat the tracheal tug can produce. Outside of the respiratory movement,upon central activation, it is an isolated signal of large amplitudethat will be measured. However, the movement imposed by the tracheal tugis a movement damped by the elasticity of the tissues that connect themandible to the rest of the head and can therefore passively transmit amovement. This is therefore a relatively imperceptible reflection of thespinal drive, that is to say the diaphragm that produces the trachealtug, whereas the antagonist/agonist muscles of the mandible impart adirect movement, notably by the action of the driving branch of thetrigeminal nerve direct from the brain, i.e. the trigeminal drive. Thegyroscope enables good measurement of movements of rotation of themandible that are produced by the muscles of the mandible and that aretherefore the result of a direct action of the brain on the mandible.Combining the signals coming from the accelerometer and from thegyroscope therefore enables improved detection of the origin and thenature of the mandibular movement and therefore improved determinationof whether the human being is sleeping or not.

Preferably, the sensing unit includes a magnetometer adapted to measuremovements of the head and/or of the mandible of said subject, whichdevice or unit includes a third input for receiving a third time streamof measurement signals coming from the magnetometer, said analysis unitbeing adapted to integrate the measurement signals coming from themagnetometer with the third signals. Using a magnetometer makes itpossible to determine an absolute position of the head and of themandible.

Preferably, the sensing unit includes an oximeter and/or a thermometerand/or an audio sensor and/or an electromyography unit and/or a pulsephotoplethysmograph, said identifying device or unit including a fourthand/or fifth and/or sixth and/or seventh and/or eighth input forreceiving a fourth and/or fifth and/or sixth and/or seventh and/oreighth time stream of measurement signals coming from the oximeter,respectively from the thermometer, from the audio sensor, from theelectromyography unit, from the pulse photoplethysmograph, said analysisunit being adapted to integrate the measurement signals coming from theoximeter, respectively from the thermometer, from the audio sensor, fromthe electromyography unit, from the pulse photoplethysmograph with thethird signals. The identifying device or unit is then adapted toassociate the measurement signals coming from the oximeter and/or fromthe thermometer and/or from the audio sensor and/or from theelectromyography unit and/or the pulse photoplethysmograph with thethird signals. These measurement signals coming from the oximeter and/orthe thermometer and/or the audio sensor and/or the electromyography unitenable more measurement signals to be taken into consideration and thusmore reliable analysis of the sleep disturbances.

Preferably, the first predetermined range consisting of frequencies issituated between 0.15 Hz and 0.60 Hz inclusive, the identifying unitbeing adapted to identify first signals over a time period of at leasttwo respiration cycles of the subject, the second predetermined rangeconsisting of values being a mandible rotation movement amplitude value.That value is for example an amplitude of the order of 1/10 millimetrei.e. based on normal respiration. The frequency range between 0.15 Hzand 0.60 Hz inclusive characterizes a situation in which the head of thesubject is so to speak quasi-immobile and therefore reflects a situationin which the subject is sleeping or is falling asleep.

Preferably, the analysis unit is adapted to identify among the thirdsignals those which in the first and second streams characterizerotation of the head about at least one axis that extends through thehead of the subject. The rotation of the head will often go hand in handwith arousal, micro-arousal or cortical and/or sub-cortical activationduring sleep and indicate a sleep disturbance.

Further provided herein is a method for automated detection of sleepingstages from mandible rotational movement data preferably recorded bymeans of gyroscope. The method may be a machine learning-based methodaccording to one or more embodiments as described herein. The methodpreferably comprises the following steps:

-   -   providing sampled rotational movement data from at least 1        subject; the sampled data may be provided by one or more        sampling and processing methods as described herein;    -   feeding the provided data to a machine learning classifier to        generate prediction scores;    -   determining a sleep stage on the basis of the generated scores.

It is understood that preferred embodiments for other methods describedin the present specification are also preferred embodiments for themethod of automated sleep or sleeping stage detection. Data from themethod may be used as input for other methods or devices, which may betherapeutic in nature.

In some embodiments, the sleeping stages may include the followingclasses (sorted by increasing level of complexity):

-   -   (1) 2 Class (i.e. binary) scoring for detecting the awake or        sleeping state in a subject;    -   (2) 3 Class scoring for classifying the sleeping stage,        including the awake state, nonREM sleeping stage or REM sleeping        stage in a subject;    -   (3) 4 Class scoring for classifying the sleeping stage,        including the awake state, light sleeping (N1 and N2) stage,        deep sleeping (N3) stage or REM sleeping stage in a subject;    -   (4) 5 Class scoring for classifying all sleeping stages,        including the awake state, N1 sleeping stage, N2 sleeping stage,        N3 sleeping stage and REM sleeping stage in a subject.

Exemplary methods for achieving an automated sleeping stage detection of3 class scoring is presented discussed in Examples 18 and 19.

Aside from detection of sleeping related disorders, the systems andmethods as described herein may also be used for the following exemplaryapplications: sleeping stage detection and/or sleep quality monitoringin healthy subjects, elderly or subjects suffering from abnormalsleeping patterns. Detection of sleeping disorders, whether clinical orpsychological in nature, may allow for tailoring treatments or to asubject's need. Moreover, studying the impact on sleep behaviour onclinical outcomes in a chronic disease may allow for gaining novelinsights about said disease and also about the treatments efficacy.Additionally, the system as described herein may also be used incombination with other systems or methods. These systems may optionallybe therapeutic in nature, such as a breathing apparatus (CPAP, BiPAP,Adaptive Support Ventilation), a mandibular advancement orthosis, and anoral device, a device for stimulating nerves and/or muscles whethertranscutaneous or implanted, a device for correcting the posture and/orposition of the body and/or head during sleeping. In some embodiments analarm can be coupled to the system or the system may be connected to orprovided with a device having an alarm function.

Additionally or alternatively, the present invention may be described byway of the following numbered embodiments. In these numberedembodiments, the term “combination” is equivalent to the term “system”,unless the context clearly indicates otherwise.

Embodiment 1. Combination comprising a sensing unit and a device forprocessing data, e.g. processing unit, relating to disturbances that mayoccur during the sleep of a subject, which sensing unit includes anaccelerometer, adapted to measure movements of the head and/or of themandible of a subject, and a gyroscope, adapted to measure movements ofthe mandible of that subject, said sensing unit being adapted to producemeasurement signals based on the measurements effected, which deviceincludes first and second inputs for receiving a first, respectively asecond, time stream of measurement signals coming from theaccelerometer, respectively the gyroscope, characterized in that thedevice includes an identifying unit adapted to identify in the first andsecond measurement signal streams first signals the frequency of whichis situated in a first predetermined frequency range and second signalsin which the value of at least one intrinsic characteristiccharacterizing a movement of the head and/or of the mandible is situatedin a second predetermined range consisting of values, said firstpredetermined frequency range and said second predetermined rangeconsisting of values being frequencies, respectively values, ofmovements of the head and of the mandible of said subject thatcharacterize a sleeping state of said subject, said identifying unitbeing adapted to produce a triggering signal after observing that thefirst and second signals that have been identified in the first andsecond streams are present for a first predetermined time period, saididentifying unit being also adapted, after it has produced thetriggering signal, to identify in the first and second measurementsignal streams third signals in which the frequency and/or the value ofsaid at least one intrinsic characteristic represents a movement of themandible and/or a change of the position of the head of said subject,said identifying unit being connected to an analysis unit adapted to beactivated under the control of the triggering signal, said analysis unitbeing also adapted to compare the third signals to profiles thatcharacterize frequencies and/or values linked to sleep disturbances andto produce a result of that comparison.

Embodiment 2. Combination according to embodiment 1, characterized inthat the sensing unit includes a magnetometer adapted to measuremovements of the head and/or of the mandible of said subject, saiddevice or unit including a third input for receiving a third time streamof measurement signals coming from the magnetometer, said analysis unitbeing adapted to integrate the measurement signals coming from themagnetometer with the third signals.

Embodiment 3. Combination according to embodiment 1 or 2, characterizedin that the sensing unit includes an oximeter and/or a thermometerand/or an audio sensor and/or an electromyography unit and/or a pulsephotoplethysmograph, said identifying device or unit including a fourthand/or a fifth and/or a sixth and/or seventh and/or an eighth input forreceiving a fourth and/or fifth and/or sixth and/or seventh and/oreighth time stream of measurement signals coming from the oximeter,respectively from the thermometer, from the audio sensor, from theelectromyography unit, from the pulse photoplethysmograph, said analysisunit being adapted to integrate the measurement signals coming from theoximeter, respectively from the thermometer, from the audio sensor, fromthe electromyography unit, from the pulse photoplethysmograph into thethird signals.

Embodiment 4. Combination according to any one of embodiments 1 to 3,characterized in that the first predetermined range consisting offrequencies is situated between 0.15 Hz and 0.60 Hz, the identifyingunit being adapted to identify first signals over a time period of atleast two respiration cycles of the subject.

Embodiment 5. Combination according to any one of embodiments 1 to 4,characterized in that the second predetermined range consisting ofvalues includes at least one head movement amplitude value thatindicates a change of position of the head.

Embodiment 6. Combination according to any one of embodiments 1 to 5,characterized in that the analysis unit is adapted to identify among thethird signals those which in the first and/or second stream characterizerotation of the head about at least one axis that extends through thehead of the subject.

Embodiment 7. Combination according to any one of embodiments 1 to 6,characterized in that the identifying unit is adapted to identify in thefirst and second signal streams movements that characterize a movementof the mandible and a change of the position of the head of the subject,said analysis unit being adapted to remove from the movement signalstreams at least one characteristic to be used to identify informationthat characterizes said movement.

Embodiment 8. Combination according to any one of embodiments 1 to 7,characterized in that the processing device is adapted to applypre-processing to the first and/or second stream by applying thereto aband-pass filter and/or a low-pass filter and/or a exponential mobilemean and/or calculation of the entropy of the frequency of the signals.

Embodiment 9. Combination according to embodiment 7 or embodiment 8 whendependent on embodiment 7, characterized in that the analysis unit isadapted to verify whether during a second time period, in particular aperiod of 30 seconds, said at least one characteristic to be used toidentify information that characterizes said movement has a value thatcharacterizes a sleeping state, respectively a waking state, saidanalysis unit being adapted to produce a first data item indicating asleeping state, respectively a waking state, if said at least onecharacteristic to be used to identify information that characterizessaid movement and that is removed from the analyzed signals of the firstand second streams received has a value that describes the sleepingstate, respectively the waking state.

Embodiment 10. Combination according to any one of embodiments 7, 9 or 8when dependent on embodiment 7, characterized in that the analysis unitis adapted to verify whether during a second time period, in particulara period of 30 seconds, said frequency and/or at least onecharacteristic to be used to identify information that characterizessaid movement and that is removed from the analyzed signals of the firstand second received streams has a value that characterizes an N1sleeping state, respectively an REM sleeping state, said analysis unitbeing adapted to produce a second, respectively a third, data itemindicating an N1 sleeping state, respectively an REM sleeping state, ifsaid frequency and/or at least one characteristic to be used to identifyinformation that characterizes said movement and that is removed fromthe analyzed signals of the first and second received streams has avalue that represents an N1 sleeping state, respectively an REM sleepingstate.

Embodiment 11. Combination according to embodiment 7, 9 or 10,characterized in that the analysis unit is adapted to verify whetherduring a second time period, in particular a period of 30 seconds, saidat least one characteristic to be used to identify information thatcharacterizes said movement and that is removed from the analyzedsignals of the first and second received streams has a value thatcharacterizes an N2 sleeping state, respectively an N3 sleeping state,said analysis unit being adapted to produce a fourth, respectively afifth, data item indicating an N2 sleeping state, respectively an N3sleeping state, if said at least one characteristic to be used toidentify information that characterizes said movement and that isremoved from the analyzed signals of the first and second receivedstreams has a value that represents an N2 sleeping state, respectivelyan N3 sleeping state.

Embodiment 12. Combination according to any one of embodiments 1 to 11,characterized in that said analysis unit is adapted to verify whetherduring a third time period, in particular a period between 3 and 15seconds, at least one intrinsic characteristic of the analyzed signalsof the first and second received streams has a level that characterizescortical, respectively sub-cortical, activity, said analysis unit beingadapted to produce a sixth data item indicating cortical, respectivelysub-cortical, activity, if said at least one intrinsic characteristic ofthe analyzed signals of the first and second received streams has alevel that represents cortical, respectively sub-cortical, activity.

Embodiment 13. Combination according to any one of embodiments 1 to 12,characterized in that said analysis unit is adapted to verify whether atleast one intrinsic characteristic of the analyzed signals has a levelthat characterizes an obstructive apnoea, an obstructive hypopnoea,respectively a respiratory effort linked to arousal, a central apnoea, acentral hypopnoea, said analysis unit being also adapted to produce aseventh, respectively eighth and ninth data item indicating obstructiveapnoea, hypopnoea, respectively a respiratory effort linked to arousal,central apnoea, central hypopnoea, if said at least one intrinsiccharacteristic of the analyzed signals of the first and second streamshas a level that describes obstructive apnoea, obstructive hypopnoea,respectively a respiratory effort linked to arousal, central apnoea,central hypopnoea.

Embodiment 14. Combination according to any one of embodiments 1 to 13,characterized in that the identifying unit is adapted to identify in thefirst and second streams values of frequency and/or of at least oneintrinsic characteristic that shows a variability not observed during asleeping state and to produce a neutralization signal on observing suchvariability and to supply the neutralization signal to the analysis unitin order to neutralize it.

Embodiment 15. Combination according to any one of embodiments 1 to 14,characterized in that the analysis unit is adapted to verify if at leastone intrinsic characteristic of the analyzed signals of the first andsecond streams has increased beyond at least 1 mm, at a frequencyestablished in a range of 0.5 to 5 Hz during at least three respiratorycycles when the movement is phasic, or beyond 1 mm in a sustained, tonicmanner for at least 2 seconds, and to produce a tenth data itemindicating bruxism during such verification.

Embodiment 16. Combination according to any one of embodiments 1 to 15,characterized in that the analysis unit is adapted to capture one ormore values in the first and second streams that give access to thecalculation of the loop gain, of the muscular gain mobilizing themandible during apnoea or hypopnoea or a period of effort, from thepoint of passive collapsibility after activation and/or from the pointof arousability before activation.

EXAMPLES Example 1

In a first example, reference is made to FIG. 1 . FIG. 1 shows a systemaccording to the invention. The system includes a sensing unit 1 and adevice 10 for processing data, preferably a processing unit, relating todisturbances that can occur during the sleep of a subject. The sensingunit includes an accelerometer 2 adapted to measure movements of thehead and/or of the mandible of the subject, preferably in threedimensions. The sensing unit also includes a gyroscope 3 adapted tomeasure rotation movements of the mandible of the subject, preferably inthree dimensions. According to one preferred embodiment, the sensingunit 1 also includes a magnetometer 4, in particular in compass form,and/or an oximeter 5 and/or a thermometer 6 and/or an audio sensor 7and/or an electromyography unit 8 and/or a pulse photoplethysmograph 9.Other sensors, such as a perspiration sensor or a nasal pressure sensor,may also form part of the sensing unit. The pulse photoplethysmographfunctions by transmission or by reflection and gives access to thecalculation of the frequency of the pulse and of the change of arterialtonus.

The sensing unit is preferably of small size, for example at most 5 cmlong, 2 cm thick and 1 cm high, in order not to interfere with thenormal sleep of the subject. The sensing unit is preferably of verysmall overall size, light in weight and flexible, enabling goodergonomics. The signals produced by the sensing unit are very suitablefor decoding using artificial intelligence. The diagnostic power of themeasurement obtained by the sensing unit is comparable to that ofcomplete polysomnography recording. The movements of the mandible maypreferentially occur on an axis, for example on an anteroposterior axis,whereas the head of the subject is turned to the right. Movements onother axes may equally be measured. The sensing unit is preferablyintended to be used only once for reasons of hygiene, but it may ofcourse be reconditioned and reused.

The position of the head is preferably determined on the basis of valuesmeasured along the three axes by the accelerometer 2. As theaccelerometer measures values of acceleration relative to terrestrialgravity, it is preferred to integrate these measured values over time inorder to obtain positions of the head which moreover will be relativepositions if there was no initialization phase during application of thesensing unit to the head of the human being. The position may beexpressed according to the value of the pitch, roll and yaw angles, ofthe Euler angles, or again by tranches of 15°, for example. The positionof the head may also be expressed in the following terms: standing,lying down, left, right, on the back.

-   -   The table below shows various angle values and the head        positions deduced therefrom:

PITCH ROLL YAW POSITION 80°  0° 10° Upright 10° 10° 70° Lying down, headon left side 20°  0° 15° Lying down, head on back

The magnetometer 4 will be added to sense the orientation of the head,in particular when the movement occurs perpendicularly to the gravityvector. Combining the values measured by the accelerometer and themagnetometer enables calculation of the movement distance and thus anabsolute value of the position of the head to be obtained.

As for the movements of the head, the movements of the mandible aremeasured with the aid of measurements from the accelerometer 2,preferably on the three axes. The movements of the mandible are alsomeasured with the aid of the gyroscope 3.

The movements of the head and of the mandible and the resulting changesof position are of different kinds. For the mandible, the movements arefor example movements of rotation at the respiratory frequency. However,latero-lateral movements are possible during sleep in the event ofbruxism or chewing, or in the event of oral dyskinesias, and there againthe condyle of the mandible is subjected in rotations in the glenoidcavity of the temporomandibular articulation, but these are not aboutthe same axes as in the event of respiratory movements.

For the head, the outcome of the movement is stochastic, i.e. theposition that the head will occupy at the end of the movement cannot bepredicted after activation. The amplitudes of the movements and of thechanges of position have different values. Accordingly, if the amplitudeof the movements of the head is high, the changes of position of themandible measured by the gyroscope are not studied, because if such werethe case, the subject is awoken and no information will be obtained onthe sleep disturbances of the subject. Small amplitudes of movement ofthe mandible captured by the gyroscope are observed when they originatein a respiratory movement. A change of the yaw angle is to be related tothe head and indicates a rotation of the head from left to right. Achange of the pitch angle is to be related to the head in flexion orextension over and above the fact that it provides information on themovement of the mandible albeit using other parameters. These values ofthe captured signals will be analyzed with the aid of the analysis unit,as described hereinafter.

A mandibular movement may be imposed as much by a respiratory movementas by a non-respiratory movement. Thus movement of the head when thehuman being is sleeping may cause mandibular movement. Mandibularmovement may be produced by the tracheal tug or by the brain of thehuman being. The tracheal tug is the traction exerted by the thorax onthe head of the human being. That traction is at the respiratoryfrequency of that human being. Thus if the head moves at the respiratoryfrequency, the mandible, which is attached to the head, will follow thatmovement imposed by the head, and will do so at the respiratoryfrequency. This is a passive movement that follows that of the head.Mandibular movement may equally be controlled directly and actively bythe brain, and in this case the head will not move. When the brain iscontrolling mandibular movement, it is the muscles of the mandible thatare directly stimulated. It is therefore useful to be able to make aclear distinction between a mandibular movement controlled by the brainand by the tracheal tug.

A distinction is made between isolated mandibular movements (IMM) at thetime of activation of the brain, for example at the end of a period ofrespiratory effort, during a cough, or spitting, or again when talkingin one's sleep, and respiratory mandibular movements (RMM) caused by therespiration of the subject. There are also mandibular movements that arecaused by bruxism or chewing. RMM type mandibular movements arecontrolled directly by the brain of the subject and do not lead tomovement of the head. An RMM type movement may also be produced by thetracheal tug and will then be combined with a movement of the head atthe respiratory frequency. When an RMM type movement stops, isnormalized or starts, it is useful to observe, with the aid of themeasurements effected by the accelerometer, if the head moved on thatoccasion. A bruxism type movement very often follows on from anactivation that has caused the head to move and that the accelerometerwill indicate, because it indeed captures this movement of largeamplitude that contrasts with the relatively fine rotatory movement ofthe mandible that the gyroscope shows clearly.

The device 10 according to the invention for processing data relating tosleep disturbances includes a first input 11-1 for receiving a firsttime stream F1 of measurement signals coming from the accelerometer 2,i.e. measured acceleration data. It includes a second input 11-2 forreceiving a second time stream F2 of measurement signals coming from thegyroscope 3, i.e. measured rotational movement data. It may also includea third input 11-3 for receiving a third time stream F3 of measurementsignals coming from the magnetometer 4, i.e. magnetic field data. Whenthe sensing unit also includes an oximeter, said identifying device willinclude a fourth input adapted to receive a fourth time stream F4 ofmeasurement signals coming from the oximeter, i.e. oximeter data. Whenthe sensing unit also includes a thermometer, said identifying devicewill include a fifth input adapted to receive a fifth time stream F5 ofmeasurement signals coming from the thermometer, i.e. thermometer data.When the sensing unit also includes an audio sensor, said identifyingdevice will include a sixth input adapted to receive a sixth time streamF6 of measurement signals coming from the audio sensor, i.e. audio data.When the sensing unit also includes an electromyography unit, saididentifying device will also include a seventh input adapted to receivea seventh time stream F7 of measurement signals coming from theelectromyography unit, i.e. electromyography data. When the sensing unitalso includes a pulse photoplethysmograph, said identifying device willalso include an eighth input adapted to receive an eighth time stream F8of measurement signals coming from the pulse photoplethysmograph, i.e.photoplethysmography data. In other words, measurement data from thevarious sensors is sent from the sensors to the analysis unit via a datalink.

The various inputs must not be physically different, because the variousstreams may be time-division multiplexed and/or each carried by acarrier wave of different frequency. Accordingly, the various inputstreams may be sent over a single data link.

The device includes a data analysis unit including an identifying unit12 adapted to identify in the first and second measurement signalstreams F1 and F2 first signals the frequency of which is situated in afirst predetermined range consisting of frequencies and second signalsthe value of which is situated in a second predetermined rangeconsisting of values, said first predetermined range consisting offrequencies and said second predetermined range consisting of valuesbeing frequencies, respectively values, of movements of the head and ofthe mandible of said subject that characterize a sleeping state of saidsubject. When the sensing unit includes a magnetometer 4, theidentifying unit 12 will also be adapted to identify in the third streamF3 of measurement signals third signals the value of which is situatedin a third predetermined range of values of the orientation of the headof said subject such as may be observed during sleep. The identifyingunit is adapted to produce a triggering signal after observing that thefirst and second signals that have been identified in the first andsecond streams are present during a first predetermined time period. Theidentifying unit is also adapted, after it has produced the triggeringsignal, to identify in the first and second measurement signal streamsthird signals the frequency and/or the value of which characterizes amovement of the mandible and/or a change of the position of the head ofthe subject. The identifying unit is connected to an analysis unit 13adapted to be activated under the control of the triggering signal. Theanalysis unit is also adapted to compare the third signals to profilesthat characterize frequencies and/or values linked to sleep disturbancesand to produce a result of that comparison.

In particular, the identifying unit may be comprised in a data analysisunit that also comprises a memory unit. The memory unit is configuredfor storing N mandible movement classes, wherein N is an integer largerthan one, and wherein at least one of the N mandible movement classes isindicative of a sleep disordered breathing event. Each j^(th) (1≤j≤N)mandible movement class comprises of a j^(th) set of rotational values,each j^(th) set of rotational values being indicative of at least onerate, rate change, frequency, and/or amplitude of mandibular rotationsassociated with the j^(th) class. Additionally, each j^(th) mandiblemovement class optionally comprises a j^(th) set of acceleration valuesand/or a j^(th) set of magnetic field data values. The data analysisunit comprises a sampling element configured for sampling the measuredrotational movement data, and optionally the measured acceleration dataand/or the measured magnetic field data, during a sampling period,thereby obtaining sampled rotational movement data and optionallysampled acceleration data and/or sampled magnetic field data. The dataanalysis unit is configured to derive a plurality of measured rotationalvalues from the sampled rotational movement data; and, optionally toderive a plurality of measured acceleration values and/or measuredmagnetic field values from the sampled acceleration data and/or thesampled magnetic field data. The data analysis unit is furtherconfigured for matching the measured rotational values with the Nmandible movement classes. Optionally, the data analysis unit is furtherconfigured for matching the measured acceleration values and/or magneticfield values with the N mandible movement classes. Thus, sleepdisordered breathing event are effectively detected.

Regarding the data link: the device and the sensing unit preferablycommunicate with each other wirelessly, but it goes without saying thata cable connection is equally possible. The device is preferably part ofa computer situated in a data processing centre. Wireless communicationis effected for example with the aid of a telephone communicationsnetwork and the sensing unit is for example fitted with a Bluetoothsystem enabling it to communicate with a telephone. Thus the streams ofmeasurement signals produced by the sensing unit will be transmitted tothe device.

The invention is based on the fact that it has been observed that themovement of the mandible is not determined only by the movement of thethorax, as the literature indicates, but also by direct control from thenerve centres of the brain that control the muscles that are attached tothe mandible and the role of which is to position it. It has beenobserved that the position of the head, and above all a change thereofduring sleep, could stop all mandibular movement or start that movementin a manner entirely independent of the thoracic movement. That is tosay that the mandibular movement can follow in the presence of athoracic movement only if the position of the head allows it and has notimmobilized it. The movement of the head can therefore action themandibular movement or paralyze it and in this sense can be nothingother than the epiphenomenon of a cerebral activation that marks themicro-arousal or arousal and that may have other effects on themandibular movement.

Movement of the head in fact affects the permeability of the upperrespiratory tracts, either by exerting crushing forces when they aremore collapsible in a sleeping situation, or by activating/deactivatingmuscle motor units of the upper respiratory tracts. These movements ofthe head during sleep modify the permeability of the upper respiratorytracts and must be known and superimposed in time on the movements ofthe mandible. These movements of the mandible can therefore be analyzedcorrectly and then interpreted in terms of respiratory control variationstarting from the air flow to be produced by the sleeping subject. Inother words, sensing and analyzing the mandibular movement takingaccount of the position of the head and changes thereof during sleep,whether or not on the occasion of micro-arousals or arousal, is to takeaccount of the cerebral control for positioning or repositioning themandible by activating/deactivating the muscles attached thereto.Outside of cerebral activation, movement of the position of the head atthe respiration frequency would be produced by the tracheal tug whereasmandibular movement at the same frequency is determined directly by thenerve centres.

By actuating in the manner of a lever the mobile bone that the mandibleforms, cerebral control seeks to stiffen the upper respiratory tracts byactivating the muscles of the tongue and of the pharynx attached theretoin order to parry the apnoea. To this end, cerebral control relies onthe muscles raising or lowering, opening or closing the mouth duringsleep, at the respiration frequency. Cerebral control can also actionthe muscles that push the mandible forward, also at the respirationfrequency, or even action in a combined manner these combined musculargroups that are involved in movements in different directions.

Changes in the position of the head during sleep are often accompaniedby an arousal or a micro-arousal that can also be recorded for exampleby electrodes placed on the scalp and that record the activity of thecortex of the brain. The scalp electrode sometimes registers noactivation when there is anyway a movement of the head with a mandibularbehaviour modification. The reason for this is that the activation hasremained sub-cortical and sometimes purely autonomic in the cerebraltrunk. These movements of the head are executed entirely independentlyof thoracic movement.

Analysis of the mandibular movement in the vertical plane and in thehorizontal plane as a function of the position of the head which can, bycreating contortion of the neck because this position of the head is nolonger aligned with that of the body or because the change in theposition of the head is the epiphenomenon of spontaneous ornon-spontaneous turning over under the control of the nerve centres,provide information on the level of respiratory effort, in particularits amplitude, that control by the nerve centres of the brain employs onthe occasion of the change of resistance to the flow of the air streamsthrough the upper respiratory tracts. The respiratory event isconsidered as an increase of effort when control from the nerve centresincreases, and is considered as central when control by the nervecentres decreases. Cerebral control to enable the organism to exit theapnoea must activate the mandibular lever upward in the vertical planeand forward in the horizontal plane, ideally with the head in axialalignment with the body in order to prevent any compression of the upperrespiratory tracts. The (micro)-arousal itself is identified by anisolated large mandibular movement (IMM) and its duration is measuredand clearly distinct from the mandibular movements that follow, whetherrespiratory or non-respiratory.

Example 2

In a further example, reference is made to FIGS. 2A and 2B.

It is on the state of cerebral control that, during sleep, the result ofthe analysis effected on the measurement data streams coming from thesensing unit provides information, and the change of position of thehead, indicated by the signals coming from the accelerometer, is oftenthe marker of its change of state. FIGS. 2A+B show streams during achange of the position of the head of a human being lying in bed. Thismovement can in no way be superposed on the movement of the mandible inthe awake and therefore conscious state during mastication, phonation ordeglutition as studied by practitioners of an art other than thatreserved to sleep medicine. The latter concern problems withmastication, phonation and deglutition studied in dentistry,stomatology, maxillo-facial surgery, orthodontics, orthodontopedics,logopedics, etc. in a conscious subject not in a sleeping state.

FIG. 2A shows, from left to right, firstly a change of the head from afirst position, in which the head is turned to the left, to a secondposition, in which the head is turned to the right. Thereafter is seen achange to a third position in which the head is again turned to theleft. The first stream F1, which is that produced by the accelerometer,relates to the three axes (Fx, Fy, Fz) of the three dimensions in whichthe measurement is effected. The second stream F2, produced by thegyroscope, also relates to the three axes. At the moment when the headturns it is clearly seen that the two streams have a peak of highamplitude. It is also seen that when the head is in the first positionthe streams F1 and F2 have, in particular in the vertical direction yfor the stream F1, a greater variable amplitude that indicates anincreased cerebral control state, indicated by the reference 1, andvariable in terms of control intensity. Moreover this is also seen inthe stream Ft, which shows the movements of the thorax. The analysisunit can therefore deduce from the streams that the person is exhibitingincreased and variable respiratory effort.

When the rotation of the head has taken place and it is in the secondposition, it is seen that the amplitude as much of the stream F1 as ofthe stream F2 has significantly decreased. The level of the stream F1 isdecreasing, which indicates that the mouth has opened, as indicated bythe reference 2. It is also seen that the air flow F5 decreases, whichcould lead to a loss of oxygen flow (reference 3). It is also seen inthe stream F2 that the amplitude has decreased, which indicates a lossof cerebral control amplitude, as indicated by the reference 4. All thisindicates that the amplitude of the effort has decreased and thatrespiration is affected (see air flow F5), which will moreover causecerebral activation and produce a command causing a new change in theposition of the head, which turns to the left. After this it is seen inthe stream F2 that the amplitude has become greater and that the flow F5has increased. It will therefore be found that the brain control tendsto normalize respiration.

FIG. 2B shows that even a small change in the position of the head iscaused by cerebral control. This FIG. 2B shows a change where a slightrotation of the head to the right has occurred. The stream F1 showsfirstly, as indicated by the arrow 1, that the cerebral control statehas increased and that a respiratory effort has been produced. It isseen that when the head changes position, the accelerometer (F1) showsan increase in amplitude and frequency that indicates cerebralactivation, indicated by the reference 2. In the streams F8 (EEG) and F7(EMG) cerebral activation is clearly seen for a period of 30 seconds,magnified here (reference 2). It is then seen that the level of thestream F1 (reference 3) shows a cerebral control state of reducedamplitude and that the mandible has been raised (the mouth has beenclosed).

The technique employed by the system according to the inventionunexpectedly and unpredictably provides information on the nature ofmandibular movement during sleep, its central origin, the control of thenerve centres that have to stiffen the pharynx to maintain ventilationand thereby oxygenation of the subject, whereas its cephalic extremitymust during sleep ideally remain in alignment with the body and inparticular with the trunk. The mandibular movement must therefore beinterpreted as a function of the position of the head and of changesthereof as otherwise why it stops or starts or changes amplitude duringsleep would not be understood.

Example 3

In a third example, reference is made to FIGS. 3A and 3B.

The techniques provided herein are applicable to the detection ofbruxism. The known diagnosis of bruxism imposes electromyography of themasseter and anterior temporal muscles and possibly anterior temporalmuscles during a polysomnographic examination in the laboratory, whichexamination has moreover to include audio-video recording. Thisexamination is costly, laborious and somewhat inaccessible, since thedemand for sleep recordings is out of all proportion to the recordingcapacities of sleep laboratories. This recording is effected during asingle night, and its laborious nature most often prevents it from beingrepeated. Also, to track bruxism, it must be possible to make recordingsover a plurality of nights because it may not be systematicallyreproduced every night and remain intermittent. It is thereforenecessary for it to be carried out at the home of the subject concerned,under real life conditions and without interfering with the naturalprogress of sleep. The result must be given quickly to optimize takingof control of bruxism and verifying the effects of treatment.

At present, bruxism is not detected in the home, since there is notechnical solution for doing this. The solutions proposed, such assurface electromyography of the masseter or anterior temporal muscles donot enable sure diagnosis of the affliction. In fact, the only recordingof the electromyographic activity of the masseter or anterior temporalmuscles can be affected by parasitic movements during the night orbecause the adipose medium on the muscle prevents capture of itselectromyographic (EMG) activity. Video recording enables the laboratoryto verify that the movements of the mandible and the resultingelectromyographic activity correspond to bruxism.

The technical solution proposed by the present invention consists inrecording mandibular movements with the aid of the sensing unit,preferably on the three principal axes of movement of the mandible inspace, and then to carry out algorithmic analysis of the signal with theaid of the analysis unit. That analysis enables identification ofmandibular movements that are specifically and exclusively thosedeveloped during onset of bruxism as well established by detection ofRMMA (rhythmic muscular masseter activity), that is to say phasic butsometimes only tonic activity, during surface electromyography of themasseter. The stream of signals produced by the sensing unit is analyzedon the three axes which also enables capture of the lateral-lateralmovement that may be imposed during grinding of the teeth and contributeto wear of the enamel. The mandibular movement, termed bruxism, is theresultant of concomitant action of agonist and antagonist muscles thatinvolve, not only the group of elevators of the mandible, such as theanterior temporal, but also the subhyoid and pterygoid muscles bothmedialis and lateralis.

FIGS. 3A+B show streams captured by the capture unit during bruxismaccess. The EMG activity of the muscles recorded, seen as the streamsF7D and F7G, has been verified as contributing to mandibular movement.The typical characteristics of masseter and/or anterior temporalelectromyographic activity are reflected in mandibular movements thatare also pathognomonic of bruxism. The latter are superposed, in theform of a modulated signal, on the tonic (sustained) or phasic(rhythmic) electromyographic bursts of bruxism that generate them. Theduration of the cycles or bursts can be calculated.

A period of effort, indicated by the arrow 1, before the onset ofbruxism can easily be identified by mandibular movement analysis as wellas a transitory arousal, indicated by the arrow 2, accompanied bycortical or merely autonomic, sub-cortical activation. Activation,whether cortical, for example exclusively reflected in a change ofcortical wave frequency on the EEG, as indicated by the stream F8, orsub-cortical and not visible on the EEG, is well marked by priormandibular movement and it is described in the literature that it oftenprecedes the onset of bruxism. It is noted that the masseter phasicand/or tonic activity peaks are contemporaneous with extreme positionsof the mandibular movement clearly verifying the relation betweenmuscular recruitment and movement of the mandibular mobile bone. Thereis seen in FIG. 3A in the stream F1 a period of effort, indicated by thearrow 1, followed by activation, indicated by the arrow 2, in turnfollowed by movement of the mandible caused by bruxism, indicated by thearrow 3. FIG. 3B is an enlarged view of the period of 10 secondsindicated by the arrow K top right in FIG. 3A. This FIG. 3B showssynchronicity between the activity in the EMG of the right masseter(F7D) and the left masseter (F7G) and bruxism mandibular movements.

It is seen here that the resumed activity of the stream F7 (EMG) of theright masseter (F7D) is synchronized with that of the left masseter(F7G) and that of mandibular movement caused by bruxism. The figureclearly shows, after a period of effort shown clearly on F1Z and F2X,the occurrence of changes at the respiration frequency of the positionof the mandible of abnormal amplitude. There follows in F1Z a largemovement with movement of the head and on the gyroscope F2X, after amovement marking cortical activation, four rotary movements at highfrequency (1 Hz) that correspond to an onset of bruxism. Thereafter, aperiod of effort reappears.

Movements of the head and of the mandible analyzed via their intrinsiccharacteristics, that is to say inter alia frequency characteristics andmorphological characteristics of the signal streams, may bedifferentiated as a function of their production mechanism and besequenced in time, successively. These characteristics can be observedby analyzing for example the amplitude, the area or the slope of themeasured signal. They are for example:

-   -   Movements linked to respiratory effort, then    -   Movements linked to transient cortical or sub-cortical        activation, then    -   Movements linked to bruxism or chewing movements that can be        clearly differentiated, such as for example the number of bursts        during the bruxism cycle, the length of the cycle between two        bursts, or the duration of the burst.

The mandibular movement is produced by the agonist/antagonist play ofthe muscles for raising and lower the mandible. The latter are directlycontrolled by the cores of the cerebral nerve centres of the trigeminaldrive branch. Here the mandibular movement can be sensed by changes ofthe angle that the mandible exhibits during its movement relative to aplane, for example during its vertical movement relative to thehorizontal plane.

-   -   Mandibular movement may begin or stop only on the occasion of a        change of the position of the head, even if thoracic movements        continue. A change of head position is always contemporaneous        with a cortical or sub-cortical micro-arousal and therefore        disturbance to control by the cerebral nerve centres. The        mandibular movement may also continue at the respiration rate        during sleep even if there is no longer movement of the thorax        of the abdomen, that is to say even if the diaphragm muscle that        actions the expansion of the thorax and of the abdomen during        inspiration controlled by the spinal nerves is no longer        functional or has stopped. The mandibular movement may then be        exerted in another plane, for example the horizontal plane, in        the form of a front to rear or rear to front movement, i.e. in a        plane other than that of the rostro-caudal traction whereby the        tracheal tug would be affected.

There can be seen in the first stream supplied by the accelerometer,likewise the second stream supplied by the gyroscope, the tonico-phasicmovement at the respiration frequency of the mandibular position that isupward i.e. in a direction opposite that which the traction produced bythe tracheal tug would exert. This upward and also forward movement isrespectively produced by the anterior temporal and masseter muscles andby the contraction of the pterygoid muscles, and in particular by theupper muscle group.

-   -   When the respiratory effort commences and when the amplitude of        the mandibular movement will increase because of the increased        central respiratory control, the direction imposed on the        mandibular movement may then also lie in a plane other than the        vertical plane that was the plane of the tug. This is owing to        the action of certain muscle groups that are recruited more than        others, such as for example the pterygoid groups that are        recruited more than the subhyoid groups. The movement at the        respiratory frequency may occur in a more horizontal direction        that will be captured by the inertial unit. The inertial unit        comprises the accelerometer and the gyroscope. In fact, if the        effort is monitored only in the vertical plane, periods of        effort could escape signal analysis. The movement can also occur        predominantly in one direction (vertical or horizontal) rather        than in another.    -   The shape of the respiratory movement, in particular its        acceleration slope, changes as a function of the muscle groups        recruited. During a vertical movement, when the masseters are        active, the direction of the movement during inhalation is        upward, in a direction opposite that observed when the        antagonist, lowering muscles dominate, and cause the decrease in        movement, and this situation can generate a change in the        movement waveform.

This analysis of the streams supplied by the sensing unit enablesverification of the fact that the movement of the mandible duringinhalation is downwards when the activity of the lowering musclesdominates and upwards when the activity of the lifting musclesdominates. This information is obtained via analysis of captured changesof speed and acceleration. This makes it possible to assess the leveland the nature of the response that the subject develops to parry therespiratory event that is unwinding and the greater or lesserrecruitment of the muscles lifting the mandible tasked with stabilizingthe upper respiratory tracts.

Example 4

In a fourth example, reference is made to FIG. 4 .

The stream observed refers to the identification of four characteristicsthat describe the behaviour of the mandible during the event. Thesecharacteristics are going to make it possible to understand how, for thesubject, in a particular stage of sleep and for a particular position ofthe head, the respiratory event is going to be constructed and how thebrain is going to respond to attempt to free itself. Above and beyondthe description of the progress of the event, it is possible to identifyinformation as to its risk of recidivism, both in the short term and inthe longer term. These characteristics have a predictive value, such asfor example, when the value of the amplitude of the response relative tothe disturbance, termed the loop gain, is high, i.e. the response to thedisturbance is high. FIG. 4 shows the loop gain. In this figure thearrow 1 marks the point of collapsibility on the stream F1, that is tosay a solution where there is no longer exercise of cerebral control sothat the mandible falls passively under the effect of local anatomicconstraints such as its weight determined for example by the obesity ofthe subject. The arrow 2 shows a movement of the mandible that ismoreover also seen at the same time in the stream 2. The peak-to-peakamplitude of the movement of the mandible, at the beginning of the arrow2, is low. Then, thereafter, while the mouth is going to open, themandible is going to be lowered, which can be seen at the level of thestream 1 which is lowered, the peak-to-peak amplitude is going toincrease. The level of the stream 1 will then reach a level indicated 3that corresponds to the arousability point which, in turn, will befollowed by a much greater amplitude peak indicated by the arrow 4. Thismovement of large amplitude enables measurement of the loop gain that isaccompanied by closing the mouth as shown by the peaks in the streams F1and F2 as well as the highest value that the stream 1 will then reachalthough the mouth has closed again in the meantime. The loop gainindicates the response to the disturbance. It is calculated as the ratioof the differences between the noteworthy points indicated by the arrows4 and 3 to the numerator and the arrows 3 and 1 to the denominator.

There is a high risk of seeing the events repeat in a self-sustainingmanner, in particular in a central form, of short apnoeas. Assessing themuscular, in particular phasic, gain of the upper respiratory tractsenables prediction of the duration of the event. A low muscular gainsignifies that the event risks lasting longer than when the gain ishigh. The point of arousability, which is the lowest point of themandibular position just before the activation that terminates theevent, also enables prediction of the duration of the event. If theposition is not much lowered, there is a risk of the event repeating,sometimes cyclically. The effect of anatomical constraints, such asthose linked to weight and to local accumulation of fatty tissue in theupper respiratory tracts, may also be determined at the time of themandibular drop immediately after a micro-arousal or an arousal when thecentres are still siderated by the latter, in particular by calculatingthe position of the mandible on the basis of the values measured by theaccelerometer (collapsibility point).

Example 5

In a fifth example, reference is made to FIGS. 5 and 6 .

The stream of measurement signals produced by the sensing unit mayinclude noise affecting the measured signal and it may prove useful topreprocess the stream when received by the device. The principle of thispreprocessing is simply to produce an enhanced signal. Analysis by theperson skilled in the art has made it possible to know that during aperiod with an augmented cerebral control state the position of themandible and therefore its speed and its acceleration periodically varyabout the same value at a frequency of the same order as the respiratoryfrequency, that is to say between 0.15 Hz and 0.60 Hz. It is possible toisolate signals concerning micro-arousals by retaining only the lowerfrequencies of the band of respiratory frequencies, for example bylow-pass filtering of the measurement signals from the accelerometer andthe gyroscope. FIG. 5 shows that, by applying this preprocessing, themicro-arousals, representing an activation, are siderated relative toperiods with an augmented cerebral control state. A clear peak is seenin the signal on the occasion of each micro-arousal. Application of thispreprocessing is made possible, for example, by the application of asixth order Butterworth filter, well known in the field of digitalsignal processing.

-   -   Conversely, it is possible to set aside periods with an        augmented cerebral control state by filtering one of the        captured signals with the aid of a band-pass filter        corresponding to the respiratory frequency band. The result of        applying this kind of filter to the signal from the gyroscope is        shown in FIG. 6 . It is seen there that the value of the signal        is higher during periods of effort.

The characteristics used to identify information in the stream ofmeasurement signals are for example:

-   -   Position of the head and of the mandible (roll, pitch, yaw        angles for example)    -   Acceleration of the mandible and of the head along each axis    -   Speed of rotation of the mandible and of the head along each        axis    -   Norm of the rotation speed of the mandible and of the head about        one or more axes (in space, if the vector u has coordinates (x,        y, z), its norm is written: (x²+y²+z²)^(0.5))    -   Norm of the acceleration of the mandible and of the head along        one or more axes    -   Median of the values measured over 10 or 30 seconds or defined        by two activations    -   Mean of the values measured over 10 or 30 seconds or defined by        two activations    -   Maximum of the values measured over 10 or 30 seconds or defined        by two activations    -   Minimum of the values measured over 10 or 30 seconds or defined        by two activations    -   Standard deviation of the values measured over 10 or 30 seconds        or defined by two activations    -   Exponential mobile mean of the measured values (with a half-life        of 5, 60, 120 and 180 seconds)    -   Fourier transformation and integration across all frequencies,        over the respiratory frequency band (0.15-0.60 Hz), over the low        frequency band (0-0.10 Hz) of the measured values    -   Fourier transformation and identification of the energy maximum        frequency or the second energy maximum frequency of the measured        values    -   Shannon entropy over a 90 second window of the measured values    -   Time offset of the rotation speed and acceleration signals of        the mandible and of the head and of the other characteristics in        order to take into account the past and the future.

It is equally possible to combine the above methods with one another.

When the characteristics have been identified in the streams ofmeasurement signals, the analysis unit can proceed to analyze them. Tothis end, it will for example use artificial intelligence calling onrandom forest type algorithms. The features extracted in this way from awhole series of signal fragments the polysomnography results of whichare known are injected, in parallel with expected results, into analgorithm in order to produce a model that will enable patternrecognition type classification of new fragments.

The signal pattern is a specific state of a signal sequence, which maybe visible physically or mathematically via parameters. Patternrecognition is a process for identifying (classifying) a specificpattern in the signal with the aid of an automatic learning algorithmbased on information already acquired or statistical parametersextracted from this signal.

Deep learning is an automatic machine learning technique that involvesmodels inspired by the structure of the human brain, termed artificialneural networks. These networks are made up of multiple layers ofneurons that enable extraction of information in the data and productionof the result. This technique is very effective for unstructured typesof data, for example an image, a sequence or biological signals.

Automatic learning (or statistical learning) is an area of artificialintelligence the objective of which is to apply statistical modellingmethods that give the machine (computer) the capacity to learninformation from data in order to improve the performance thereof insolving tasks without being explicitly programmed for each of them.

Artificial intelligence (AI) is the set of technologies aimed atenabling machines to simulate intellectual activities.

The development of these models may for example proceed as follows:

-   -   1) Two hundred subjects are equipped with the sensing unit at        the same time as they undergo the reference clinical examination        in the field of sleep: polysomnography.    -   2) The signals captured from forty of these subjects are then        used to train each random forest model. The signals from the        sensing unit and a subset of the characteristics obtained after        the preprocessing step are injected conjointly with the        reference results of the examination of sleep in the random        forest algorithm and classification models are generated on the        basis of this input data.    -   3) The remainder of the subjects are then used in order to        validate the model: the signals from the sensing unit        corresponding to these subjects are injected into the model        generated in the preceding step, generating in turn results, and        those results are compared to the results obtained by way of        polysomnography. When the agreement between the results obtained        by means of the models and by polysomnography is deemed        sufficient, the models are considered valid. Otherwise,        development resumes from step 2 of this section.

In order to be able to reliably identify disturbances that occur duringthe sleep of a subject it preferred to be able to observe that thesubject has actually entered the sleep phase. When it has been detectedthat the subject is actually in the sleep phase, it will then also bepossible to establish the subject's sleep stage in order to be able tointerpret correctly the signals present in the streams of measurementsignals. On entering sleep, the mandible will assume a respiratoryfrequency between for example 0.15 Hz and 0.60 Hz. That respiratoryfrequency must be present for a plurality of tens of seconds at astretch in order to be able to affirm a stable sleep state.

Example 6

In a sixth example, various sleep stages of a subject are discussed. Inparticular, table 1 (included below, after the examples) shows thevarious sleep stages of a subject and their relation with movement ofthe mandible and movement and the position of the head of the subject.What essentially characterizes a wakening state is that in that statethe mandible moves unpredictably, whereas in a subject a sleep state ischaracterized, with no sleep disturbance, by the mandible effecting amovement of rotation at a frequency which is that of respiration. Todetect a waking state, respectively a sleeping state, the analysis unitwill preferably function using an analysis window of 30 seconds andpre-processing of the first and second streams using a band-pass filterand/or an exponential mobile mean. To extract the profile thatcharacterizes a waking state, respectively a sleeping state, a level ofthe normalized mean will for example be taken into account. That levelis in fact higher in a waking state than in a sleeping state.

Also distinguished in sleep are N1, N2, N3 and REM (Rapid Eye Movement)stages. During the N1 sleep stage there is seen a variation of themovement of the mandible at the respiratory frequency with apeak-to-peak amplitude variability for a period often limited to a fewminutes in the adult. The position of the head generally remains stable,but that of the mandible remains unpredictable, or may changeperiodically. To detect an N1 sleep stage using the processing device ananalysis window of 30 seconds is preferably used in order to ensurecontinuity of movement. Pre-processing the first and second streams bycalculating the entropy of the frequency of the signals may be used. Thelevel of the normalized mean will be taken into account in a firstapproach as a profile that characterizes this N1 sleep stage, but otherapproaches may be used to improve analysis accuracy. In an N1 stage thelevel of the normalized mean will be higher than in N2 or N3 stages.

The analysis unit is adapted to verify if during a second time period,in particular a period of 30 seconds, said normalized mean and avariance of the amplitude and of the frequency of the first and secondstreams received have a level that characterizes an N1 sleep state. Theanalysis unit is adapted to produce a second data item indicating an N1sleep state if said normalized mean and the variance of the amplitudeand of the frequency of the first and second streams received have alevel that characterizes a sleep state N1.

During the N2 and N3 sleep stages, the variation of cerebral controlamplitude and/or frequency is increasingly low from N2 to N3. There willtherefore be virtually no movement of the mandible or of the head atthese stages in a normal subject. To detect an N2 or N3 sleep stage withthe aid of the processing device an analysis window of 30 seconds willpreferably be used in order to ensure continuity of movement.Pre-processing with the aid of a low-pass or band-pass filter will alsopreferably be used. The level of the normalized mean will be taken intoaccount in a first approach as a profile for characterizing this N2sleep stage. In an N2, respectively N3, stage the level of thenormalized mean will be less and less high. The level of the normalizedmedian may also be used to identify the N2 or N3 stages or otherstatistical measuring techniques.

The analysis unit is adapted to verify if during a second time period,in particular a period of 30 seconds, said normalized mean and/or anormalized median of the first and second streams received has or have alevel that characterizes an N2 sleep state, respectively an N3 sleepstate. The analysis unit is adapted to produce a fourth, respectivelyfifth, data item indicating an N2 sleep state, respectively an N3 sleepstate, if said normalized mean and/or a normalized median for example ofthe first and second streams received has a level that characterizes anN2 sleep state, respectively an N3 sleep state.

In the human being an REM stage is characterized by unpredictablemovements of the mandible. To detect this kind of stage with the aid ofthe processing device an analysis window of 30 seconds will preferablybe used in order to ensure continuity of movement. In adults, this typeof movement of unpredictable frequency and/or amplitude often lastslonger in REM than in the N1 stage. The periods of such movement duringan N1 stage are often limited to a few minutes. The direction ofmovement of the mandible position during cerebral activation is oftennegative, because the mouth opens. In the REM stage there is seen avariation of the movement of the mandible at the respiratory frequencywith a variability of the peak-to-peak amplitude that is not periodic.The position of the head generally remains unchanged during the REMstage. Detection is effected in an analogous manner to that for the N1period and the aim is to observe the respiratory instability of themovement of the mandible. The REM stage is often entered withoutcortical activation that the EEG could capture and with no movement ofthe head. The accelerometer will therefore not measure anything, whereasthe gyroscope will observe changes in rotation of the mandible. Thisshows the importance of having both the signal from the gyroscope andthat from the accelerometer in order to correctly observe entry into theREM phase. Exit from the REM phase often goes hand in hand with cerebralactivation that will be observed by the accelerometer and the gyroscope,which will observe an isolated mandibular movement (IMM) and whereapplicable a movement of the head. The level of the normalized mean maybe taken into account as a first approach. The variance of the amplitudeand of the frequency will also be looked for, for example. Detection ofREM during the first fifteen minutes of sleep enables a diagnosis ofnarcolepsy.

Example 7

In a seventh example, reference is made to FIG. 14 .

Comparative analysis throughout sleep for example of the variance of thevalues of amplitude and/or of frequency of movement of the mandibleand/or other statistical characteristics of the signal, in isolation orgrouped into an array to which a classifier, for example of randomforest type, is applied to practice statistical inference, enablesdifferent stages to be distinguished. To this end, FIG. 14 showsspectrograms of the distribution of the mandibular movement frequenciesfor differentiating the stages. In this FIG. 14 the vertical axisrepresents an amplitude density and the horizontal axis a frequency.These specific characteristics of each sleep stage can also beidentified by machine deep learning. This algorithmic and/or statisticalapproach may also be used for the characterization of respiratory eventsand non-respiratory motor events.

The table below gives an example of the variance of the amplitude levelof the mandible rotation signal between the various stages of sleep. Inthis table, by “interval” is meant the interval between the upper level,at the 2.5^(th) centiles, and the lower level, at the 97.5^(th)centiles. By “amplitude” is meant the difference between the maximum andminimum values. The “variance” is a measurement of the spread of thevalues considered. The measurements are based on 1000 samples taken overa period of 30 seconds for each stage.

Stage Variance (mm²) Interval (mm) Amplitude (mm) N1 0.016 to 0.0640.378 to 0.945 0.470 to 1.070 N2 0.018 to 0.052 0.465 to 0.795 0.560 to0.990 N3 0.081 to 0.143 0.899 to 1.245 1.020 to 1.330 REM 0.004 to 0.0980.250 to 1.370 0.320 to 1.580

The analysis unit being adapted to verify if during a second timeperiod, in particular a period of 30 seconds, said normalized mean and avariance of the amplitude and of the frequency of the first and secondstreams received have a level that characterizes an REM sleep state,said analysis unit being adapted to produce a third data item indicatingan REM sleep state if said normalized mean and the variance of theamplitude and of the frequency of the first and second streams receivedhave a level that characterizes an REM sleep state.

The identifying unit is adapted to identify in the first and secondstreams movement signals that characterize a rotation of the mandibleand/or a movement of the head of the subject. The analysis unit isadapted to analyse these signals, for example by applying to thesemovement signals a band-pass filter and an exponential mobile mean or ameasurement of the entropy of the frequency of the signals. By applyingthis band-pass filter, for example to respiratory frequencies, and thisexponential mobile mean, with for example a half-life equal to 5, 60,120 or 180 seconds, to the first and second streams of signals suppliedand with a first observation time period of 30 seconds, the analysisunit will be able to observe if the signal is unstable. If this is thecase an arousal situation will be observed. If on the other hand thesignal is stable a sleep situation could be observed.

The analysis unit is adapted to apply as a profile that characterizes asleep state said exponential mobile mean over a second time period ofthe first and second streams situated between 30 seconds and 15 minutes,in particular 3 minutes. For some analyses the second time period couldeven be 30 minutes. The analysis unit is adapted to verify whether ornot during said second time period said exponential mobile mean has asubstantially constant value and to produce a first data item thatindicates a sleeping state, respectively a waking state, if said valueis substantially constant, respectively not constant. The identifyingunit is adapted to identify in the first and second streams movementsignals that characterize a rotation of the mandible and of the head ofthe subject. The analysis unit is adapted to calculate the entropy onthe frequencies of these movement signals. By applying this entropyfunction, with for example an analysis window of 90 seconds, to thefirst and second streams of signals supplied and with an observationtime period of 30 seconds, the analysis unit could then observe thelevel of the normalized mean. If the level is high, an N1 or REM sleepsituation will be observed as a function of the value of the level.

The identifying unit is adapted to identify in the first and secondstreams movement signals that characterize a rotation of the mandibleand/or a movement of the head of the subject. The analysis unit isadapted to apply to these movement signals a band-pass filter or alow-pass filter. By applying this band-pass filter for example atrespiratory frequencies or this low-pass filter (below 0.10 Hz forexample) to the first and second streams of signals supplied and with anobservation time period of 30 seconds, the analysis unit will be able toobserve the level of the normalized mean and/or of the median. As afunction of the level an N2 or N3 sleep situation will be observed.

Cerebral activations in the form of a micro-arousal have a durationbetween 3 and 15 seconds inclusive and may be of cortical orsub-cortical type. Cerebral activations that lead to arousal last morethan fifteen seconds. Cortical cerebral activations in REM sleep mayhave by way of characteristics repeated lowering of the mandible. In thecase of cortical activations, a corticobulbar reflex is activated and aplurality of sudden movements of great amplitude or even of greatduration of the mandible is observed. The reflex amplifies themovements. In the case of sub-cortical activations, this reflex is notactivated, and it is then possible to observe only one sudden movementof lesser amplitude with a frequency discontinuity relative to therespiratory frequency at which the mandible was actioned. This movementmay be of much lower amplitude and shorter duration than when thecortico-bulbar reflex is activated. The movement is therefore often lessmarked and identifying it may be assisted by the detection of aconcomitant movement of the head that may be exerted over only a veryshort distance.

Example 8

In a further example, reference is made to table 2. Table 2 showscortical and sub-cortical cerebral activation characteristic. Aconsequence of cortical activation will be abrupt closing or opening ofgreat amplitude of the mandible for a duration situated between 3 and 15seconds. If this cortical activation occurs during sleep, it willgenerally be accompanied by a change of position of the head of thesubject. The analysis unit will analyse the amplitude and the durationof the movements over a window of ten seconds using the first and seconddata streams.

Sub-cortical activation is characterized by a discontinuity in thefrequency of variation of the movement of the mandible and in the shapethereof. The mandible will most often remain stable. The analysis unitwill analyse the amplitude and the duration of the movements over awindow of ten seconds using the first and second data streams. Theanalysis may equally be carried out on a continuous variable.

The analysis unit will therefore verify if during a third time period,in particular a period situated between 3 and 15 seconds, an amplitudeof the signals of the first and second streams received has a level thatcharacterizes cortical, respectively sub-cortical, activity. Theanalysis unit is adapted to produce a sixth data item indicatingcortical, respectively sub-cortical, activity if said amplitude of thefirst and second streams received has a level that characterizescortical, respectively sub-cortical, activity.

To detect the presence of a respiratory event or a non-respiratory motorevent, the analysis unit will analyze the evolution of the position ofthe mandible, the amplitude of the peak-to-peak mandibular movement, thevariance of the peak-to-peak amplitude of the mandibular movement thatindicates a variation of the cerebral control amplitude and thefrequency of the mandibular movement. If a low amplitude is observed,that is to say an amplitude corresponding to the amplitude observedduring the eupnic respiratory movement, and in the presence of stablecentrality (mandibular movement occurring around a continuous and stabledegree of opening of the mouth), there are no events to be taken intoaccount for sleep disturbances.

If a high respiratory control amplitude, for example an amplitudecorresponding to movements exceeding 0.3 mm, is observed, that is to saya change of amplitude greater than the change of amplitude observedduring the eupnic movement, there is deduced an increased motor orrespiratory effort that may indicate sleep disturbances.

If a large respiratory control amplitude decrease is observed, that isto say one that is low, for example of the order of 0.1 mm, or zero,with stable or unstable control centrality, for at least 10 seconds ortwo respiratory cycles for example, there is deduced a central typerespiratory event.

Measuring the gain of the muscular response of the upper respiratorytracts during the event enables determination of its obstructivecharacter, that is to say marked respiratory effort, as against itscentral character with no respiratory effort or with a reducingrespiratory effort ending up below the level considered to be normal.This analysis enables characterization of apnoea and hypopnoea asobstructive or central. The level of normality of the respiratory effortis determined beforehand during periods of normal respiration duringsleep, for each stage of sleep.

The change of position of the head can modify the configuration of theevent with or without change of sleep stage or of transition betweensleeping and waking. The gain of the muscular response during the eventis calculated by measuring the peak-to-peak amplitude change duringphasic mandibular movement at the respiratory frequency during theevent. It is the measurement of the peak-to-peak amplitude differencebetween the start and the end of the period of the event during itsphasic movement that can already be calculated from a single respiratorycycle, which supplies the gain value. The change may be minimal, of theorder of 1/10 millimetre, or even less, but can reach 3 centimetres. Thechange may be accompanied by a change in the absolute position of themandible, meaning that the mouth is more or less open when its phasicdisplacement is exerted. The change can occur in any direction betweenthe horizontal and the vertical, taking into account the position of thehead during sleep.

Example 9

In a ninth example, reference is made to table 3. Table 3 illustrates atypical behaviour of cerebral control for the detection of respiratoryevents and non-respiratory motor events. It can be seen that to detectan obstructive apnoea-hypopnea, the analysis unit will for example use amedian and/or a mean value on the first and second flow of measurementsignals. An observation time of at least two breathing cycles or 10seconds will be preferred to make the analysis more reliable.Obstructive apnoea-hypopnea is characterized by large cerebral controlamplitude at the respiratory rate that can be repeated cyclically ornon-cyclically. It will end with a large mandibular movement duringcerebral activation. In particular, the distribution of the amplitudevalues of the mandibular movement in the stream under consideration willbe analyzed.

To detect breathing effort linked to arousal (RERA), the unit ofanalysis will proceed in the same way as described in the previousparagraph. To detect a central apnoea-hypopnea the duration ofobservation will also be at least two breathing cycles or 10 seconds.

Example 10

In a tenth example, reference is made to FIGS. 4 and 7 .

A situation of bruxism will be detected by using for example the median,mean, maximum value or other statistic of the rotation of the mandibleand of its acceleration over an observation time of 30 seconds forexample.

Cerebral control following cerebral activation with or without a changein the position of the head may:

-   -   Stable and of low amplitude;    -   Increase with a high or “rising” centrality; the mouth closes        and the event is corrected if the latter was obstructive;    -   Increase with a low or “descending” centrality; the mouth opens        and the event is imposed, obstructive;    -   Decrease with a low or “descending” centrality; the event is        imposed, central;    -   Increase with a high or “rising” centrality, when the pterygoid        lateralis muscles are recruited.

If the position of the head does not change but the respiratory controllevel changes, the position of the mandible and the change thereincontinue to provide information on the respiratory control level. FIG. 7shows a signal indicating cortical cerebral activation, plotting themovement of the mandible as measured by the accelerometer and thegyroscope. In a left to right direction in this FIG. 7 there are firstseen a few oscillations indicating movement of the mandible at a regularfrequency. This movement of the mandible is caused by respiration withsome degree of effort. The subject concerned has to make an effort tocause air to pass through the upper respiratory tracts, which can beseen in the amplitude of the signal from the gyroscope. In particular,the reference 1 indicates a micro-arousal provoked by corticalactivation. There is then seen a strong oscillation indicating amovement of greater amplitude that follows on from cerebral activation.It can then be seen that the level of the signal has increased,indicating that the mouth has closed and that the mandible has risen afew tenths of a millimetre. If the mandible rises and there is stilldetected a respiratory frequency of its movement with an amplitudegreater than the normal value, it can be deduced that there is apersistent obstructive event as observed here. If the movement becomesof low amplitude, it may be stated that respiratory control is no longerrising beyond the normal and that the respiratory effort has beennormalized. The reference 2 indicates a micro-arousal caused bysub-cortical activation producing a signal of lower amplitude thancortical activation. The set of results obtained following processing ofthe signals by the analysis unit may be presented in the followingmanner for example:

-   -   Hypnogram: evolution of the stages of sleep and of the moments        of waking/sleeping transition during the recording;    -   Start and end time of the recording, time spent in bed and/or        lying down;    -   Total sleeping time; various efficacy indices;    -   Fragmentation of sleep, for example the number and index of        micro-arousals and arousals (activations), the number and index        of waking/sleeping transition changes;    -   Number and index of respiratory and non-respiratory motor        events;    -   For example, in the event of repetition of central respiratory        events with a cyclic, periodic, crescendo-descrendo variance of        the cerebral control amplitude, if the duration of the period is        measured as greater than 40 seconds, it is possible to suspect        that the type of periodic respiration is evolving, possibly in        the context of cardiac insufficiency;    -   Events of a cyclic nature can also be of an obstructive kind or        events of an obstructive kind can also repeat in a cyclical        manner (for example when the loop gain is high and/or the        arousability strong);    -   Repeated sub-cortical activations isolated with respect to        respiratory effort suggest association thereof with a limb        movement.

The position of the head impacts on the frequency and the very nature ofthe respiratory and non-respiratory motor events occurring during sleep.The change of position of the head during sleep is alwayscontemporaneous with activation from the brain. During the latter, thehead will find a new position and the mandible, which has moved with alarge amplitude through a plurality of repeated movements on theoccasion of this change, will find a new position thereafter to besubjected again to respiratory drive the amplitude of which will be ameasure of the central control level. There is therefore an associationof event, as shown in FIG. 7 . The central activation, the possiblemodification of the position of the head and the possible modificationof the position of the mandible that accompanies it and activerespiratory control with modification of the amplitude of therespiratory movement of the mandible are therefore integrated in thebrain. The relations between activity and cerebral activation areexamined. If the control activity level changes, for example via thechanged peak-to-peak amplitude of the respiratory movement of themandible, this is firstly the consequence of central activation andcontrol state changes in the cerebral trunk. Moreover, the respiratoryactivity is captured by the gyroscope during rotary movement of themandible whereas the central activation is captured by the accelerometerduring linear movement of the head.

The change of position of the head and the cerebral activation thataccompanies it determine the risk of the event occurring whether it be arespiratory or non-respiratory motor event by modifying the controllevel and therefore the type of event. A change in the position of thehead is necessarily accompanied by a cerebral activation and can modifythe conditions of flow of the air fluids in the respiratory tracts, inparticular by modifying the upper respiratory tract calibre muscularretention state conditions in addition the fact that the new orientationassumed by the head can expose those respiratory tracts to mechanicalcrushing forces.

Mandibular movement and repositioning during cortical or sub-corticalactivation may be described during the event as follows:

(1) The mandible is either passive, or the mandible drops on regressionof activation with no tonic and/or phasic support of musculature whilethe central motor control is siderated for the duration of a fewrespiratory cycles. The relaxation of its position after the mouthcloses and with passive opening of the mouth, that is to say themandible is no longer supported, because of the loss of the toniccomponent of the musculature deemed to support it, over a variabledistance but with a marked slope (> 1/10 mm/s); the measurement of thisdistance between closing of the mouth and the lowest point recordedbefore the change of slope that will follow is a marker of the passivecollapsibility of the pharynx when following on from activation there isa loss of control by the nerve centres; this situation can last for atime equivalent to a few (maximum five) respiratory cycles.

This relaxation may not take place, the mouth remaining closed orvirtually closed, because there is no loss of central control(persistence of tonic component) of the musculature controlling theposition of the mandible.

-   -   (2) The mandible then shows the muscular response gain, in a        phasic and/or tonic form, that will reposition it at the        respiratory control frequency during the event, before a new        activation is triggered. There follows from this a resumption of        the muscular activity controlling the position and the movement        of the mandible. This resumed activity of the muscles may be        manifested by a change of slope describing its new position with        or without respiratory movement, that is to say with or without        a phasic component, i.e. with at least one peak-to-peak        amplitude measurable beyond the background noise of the        measurement (>0.05 mm). This latter movement signals the        resumption of respiratory movement, that is to say a shift in        the respiration frequency, and therefore a respiratory effort        that will make it possible to qualify the obstructive event as        central or mixed, according to the rules of evaluation. The        movement of the centrality makes it possible to specify if the        degree of opening of the mouth is stable, increasing or        decreasing, whereas the peak-to-peak amplitude of the        respiratory movement reflects the current degree of effort.    -   (3) The centrality or amplitude point reached to be the lowest        and from which the movement of closing the mouth will be        executed, the first movement determined by the activation is        similar to an arousability threshold. This movement is sometimes        downward, for example in REM or when the mouth has not opened        because the respiratory effort is above all exerted by the        activity of the pterygoid lateralis and the masseter muscles        that have held the mouth in the forward and high position, this        movement bears witness to the activation. The latter may be        cortical or sub-cortical or with a sub-cortical and then        cortical sequence, or when the mandible has not opened much        during the event, plausibly because of the activity of the        pterygoid lateralis muscles it may then open suddenly during the        activation whereas most often, as the mouth had opened during        the event, the activation closes it brutally.    -   (4) There follows the mandibular position point at the greatest        distance during activation from this arousability point, as        shown in FIG. 4 . The distance separating them is a measure of        the amplitude of the mandibular movement during the activation.        That value is measured and compared with the level of        respiratory effort deployed during the event, before the        activation via the change of amplitude of the respiratory        movement since the beginning of the resumption of the effort up        to the arousability point. The ratio of these values is a        measure of a degree of mandibular loop gain.

Example 11

In an eleventh example, reference is made to FIG. 8 . FIG. 8 shows anexample of the first measurement signal stream F1 (measured by theaccelerometer) and the second measurement signal stream F2 (measured bythe gyroscope) in the situation where the subject suffers an obstructiveapnoea. In this figure F5n designates the nasal flow and F5th theoro-nasal thermal flow. It will be observed there that during the timeperiod from T1 to T2 and following the apnoea indicated by the reference1 the signal is not stable. At the start of this period it is seen thatthe signal supplied by the gyroscope is of lower amplitude than at theend of the event. Central control is intensified during the eventbecause it is necessary to combat the obstruction causing the apnoea orhypopnoea. During this same period T1-T2 it is seen that theaccelerometer (reference 2) and the gyroscope (reference 3) indicaterespiratory effort followed by cerebral activation (reference 4).

Analysis of the signals shows that in the presence of obstructive apnoeabetween T1 and T2 there is observed on the accelerometer (F1) themovement of the mandible at the respiration frequency and with anamplitude increasing from the peak-to-peak amplitude at the same time asthe mouth opens as a consequence of the position of the mandibledescending more and more from one respiratory cycle to another (A). Atthe same time (C), it is seen that the angular speed of the rotary,respiratory movement of increasing amplitude indicates that the effortitself is increasing. Note at the height of the letter B the effect ofthe activation on the movement of the mandible measured with theaccelerometer, which activation provokes an upward movement of themandible and has the consequence of the mouth closing. On this occasion,the mandible will assume a new position. At the level of the letter D onthe gyroscope, it is seen that this movement of closing the mouth is notpurely rotary. The changes of state of the signals at the height of theletter B and of the letter D are contemporaneous with resumption ofventilation on the occasion of the cerebral activation (micro-arousal).

Example 12

In a twelfth example, reference is made to FIG. 9 . FIG. 9 shows anexample of the first measurement signal stream F1 and the secondmeasurement signal stream F2 in the situation where the subject suffersan obstructive hypopnoea, indicated by the arrow 1. The arrow 0indicates an arousal state. This same FIG. 9 also shows a sixth streamF6 captured by an audio sensor that indicates the presence of snoring,together with a seventh stream F7 sensed by a chin electromyogram and aneighth stream F8 sensed by an electroencephalogram. There is seen therea series of mandibular movements (R) of greater amplitude for a periodof a few seconds which each time indicates cortical or sub-corticalactivation. These movements are concomitant with changes of peaks in thestreams F6, F7 and F8. In fact the electromyogram andelectroencephalogram signals show clearly that there is cerebralactivation on this occasion. The obstructive hypopnoea is indicated bythe arrows 2 and 3, the arrow 2 indicating an effort and an opening ofthe mouth and the arrow 3 an effort and a rotation of the mandible. Thishypopnoea is followed by an activation in the form of a micro-arousal,indicated by the arrow 4. The high value of the respiratory mandibularmovement between the micro-arousals reflects a high respiratory effortthat is moreover emphasized by snoring. It is therefore seen in thestream F1 coming from the accelerometer and in the stream F2 coming fromthe gyroscope that during snoring there is rotation of the mandible withopening of the mouth. FIG. 9 therefore shows that cerebral activity maybe registered by the accelerometer and the gyroscope that measuremandibular movements as much during the period of effort at therespiration frequency as during the cerebral activation, but in thiscase at a frequency that is no longer typically that of respiration.Still in this FIG. 9 the digit 0 indicates an arousal state of thesubject.

Example 13

In a 13^(th) example, reference is made to FIG. 10 . FIG. 10 shows anexample of the first measurement signal stream F1 and of the secondmeasurement signal stream F2 in the case where the subject suffers amixed apnoea. As in FIG. 8 , there is seen in this FIG. 10 an increasein the angular speed of the mandible at a frequency corresponding to therespiration frequency. The digit 1 indicates an absence of respiratoryflow that goes hand in hand with an absence of control and of effort,indicated by the digit 2, followed by restoration of cerebral controland effort, indicated by the digit 3.

Example 14

In a 14^(th) example, reference is made to FIG. 11 . FIG. 11 shows anexample of the first measurement signal stream F1 and of the secondmeasurement signal stream F2 in the situation where the subject suffersa central apnoea. The peaks F show a movement of the head and of themandible on resumption of respiration. It is also seen that between thepeaks F there is so to speak no movement of the mandible. The digit 1indicates an absence of respiratory flow that goes hand in hand with anabsence of effort, indicated by the digit 2, and activation andresumption of the effort, indicated by the digit 3.

Example 15

In a 15^(th) example, reference is made to FIGS. 12 and 13 . FIG. 12shows an example of the first measurement signal stream F1 and of thesecond measurement signal stream F2 in the situation where the subjectsuffers a temporary disappearance of all control of cerebral origin,which is characteristic of central hypopnoea. This disappearance ischaracterized by the mouth opening passively because it is no longerheld up by the muscles. It is therefore seen in the streams F1 and F2that between the peaks the signal does not indicate any activity. On theother hand at the moment of the peak there is observed a high amplitudeof the movement of the mandible. Toward the end of the peaks there isseen a movement that corresponds to a non-respiratory frequency, whichis the consequence of cerebral activation that will then result in amicro-arousal. The digit 1 indicates the period of hypopnoea where areduction of the flow is clearly visible on the stream F5th from thethermistor. The digits 2 and 3 indicate the disappearance of mandibularmovement in the streams F1 and F2 during the period of centralhypopnoea. FIG. 13 shows an example of the first measurement signalstream F1 and of the second measurement signal stream F2 in thesituation where the subject experiences a prolonged respiratory effortthat will terminate in cerebral activation. It is seen that the signalfrom the accelerometer F1 indicates at the location indicated by H alarge movement of the head and of the mandible. Thereafter the stream F2remains virtually constant whereas in that F1 from the accelerometer thelevel drops, which shows that there is in any event a movement of themandible, which is slowly lowered. There then follows a high peak I thatis a consequence of a change in the position of the head during theactivation that terminates the period of effort. The digit 1 indicatesthis long period of effort marked by snoring. It is seen, as indicatedby the digit 2, that the effort is increasing with time. This effortterminates, as indicated by the digit 3, in cerebral activation thatresults in movements of the head and the mandible, indicated by theletter I.

The analysis unit holds in its memory models of these various signalsthat are the result of processing employing artificial intelligence asdescribed hereinbefore. The analysis unit will process these streamsusing those results to produce a report on the analysis of thoseresults.

It was found that the accelerometer is particularly suitable formeasuring movements of the head whereas the gyroscope, which measuresrotation movements, was found to be particularly suitable for measuringrotation movements of the mandible. Thus cerebral activation that leadsto rotation of the mandible without the head changing position can bedetected by the gyroscope. On the other hand, an IMM type movement willbe detected by the accelerometer, in particular if the head moves onthis occasion. An RMM type movement will be detected by the gyroscope,which is highly sensitive thereto.

Example 16

In a further example, reference is made to an exemplary procedure forfeature extraction, data processing, and data description that is of usein the methods and devices that are provided herein. Such a procedure isschematically shown in FIG. 15 .

In particular, feature extraction, data processing and descriptive weredone in R statistical programming language (8), while Machine learningexperiments were conducted using sci-kit learn and SHAP packages inPython language.

23 different features were extracted from the mandibular movement rawsignal of each event, or each 10 seconds of normal breathing. Thesefeatures included: central tendency (mean, median and mode) of MMamplitudes; MM distribution (raw or enveloped signals): skewness,Kurtosis, IQR, 25^(th), 75^(th) and 90^(th) centiles; extreme values:Min, Max, 5^(th) and 95^(th) centiles of MM amplitudes; tendency ofvariation: Linear trend and coefficients of Tensor product-based splinefactors (S1, 2, 3, 4) from a generalized additive model to evaluate MMin function of Time; duration of each event.

The impact of the various features on the model's classification intocentral hypopneas, normal sleep, and obstructive hypopneas can bedescribed by means of the SHAP score. The SHAP score measures theaverage marginal contribution across all possible coalitions with otherfeatures to classify 3 target labels. The higher the SHAP score, themore important contribution that feature may provide. The Lundberg'sShapley additive explanation (SHAP) method unified the Shapley's scorein cooperative game theory (1953) (Lloyd S Shapley. “A value forn-person games”. In: Contributions to the Theory of Games 2.28 (1953),307-317.) and the local interpretation approach (Marco Tulio Ribeiro,Sameer Singh, Carlos Guestrin. “Why should i trust you? Explaining thepredictions of any classifier”. In: Proceedings of the 22nd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining. ACM.2016, 1135-1144.) to provide the best solution so far to explain anyblack-box model. The SHAP theory considers the input features as“players” in a cooperative game were the “payout” is making correctprediction of a target label (i.e. central or obstructive hypopnea). TheSHAP algorithm lets each feature value to join with other features inrandom order to form a coalition, then assign a payout (SHAP score) foreach feature values depending on their contribution to the totalprediction. The SHAP score is the result from averaging the change inprediction that a coalition gains when a new feature participated. Inessence, SHAP score of a feature value is the average marginalcontribution of that feature value across all possible coalitions for aparticular prediction.

In particular, the features are extracted as follows:

1. Loading a sequence of raw MM data at (e.g. sampling rate=10 or 25Hz). This sequence has a significant duration, e.g. between 30 minutesand 8 hours;

2. Marking timestamps of obstructive and central hypopnea events;

3. For each time stamp ti, perform the following steps

-   -   3.a. Check whether ti is the beginning of an Obstructive or        Central hypopnea event.    -   3.b. If ti is the beginning of an obstructive or central        hypopnea event,        -   assign ti to (t_begin), and subsequently search for the            ending (t_end); and,        -   extract the raw data sequence to a temporary holder named            “Event E”, by indexing t_begin and t_end;

4. For each event E, perform the following steps

-   -   4.a. Calculate event duration dt=(t_end−t_begin)    -   4.b. Determine the distribution of the measured parameters        during the event;        -   Min, Max, Mean, median, mode, 5^(th), 25^(th), 75^(th),            90^(th), 95^(th) centiles, Skewness, Kurtosis, IQR;        -   Fit a GAM non-linear model to estimate MM amplitude and/or            position by a spline function on time t, then extract the            coefficient of spline function;        -   Fit a simple linear model, extract the Intercept and linear            slope;        -   Concatenate all features+label by matching the measured data            with a mandible movement class.

After feature extraction, the extracted features and correspondingtarget labels were integrated to a tabular dataset.

After that, exploratory data visualization, one-way ANOVA and pairwisestudent-t tests with Bonferroni correction are performed to classify themandibular movement features in 3 groups: normal breathing, obstructiveand central hypopneas. Significance level are set at highly stringentcriteria (p=0.001) (10) for null-hypothesis testing.

For the purpose of model development, the data were randomly split into2 subsets: a larger set (70%) for model development and a smaller set(30%) for model validation. Because the original training set wasunbalanced between central (minority class) and obstructive hypopneas(majority class), a synthetic minority over-sampling technique (SMOTE,the Synthetic Minority Over-sampling Technique, which is well-known assuch) on the training set before model development was applied.

A multiclass classification rule was built to classify the 3 groupsusing 23 input features. This consisted of a Random Forest algorithmthat combined 500 distinct decision trees (each one was constructed on arandom subset of 5 features).

The content of the Random Forest model was then analysed in order toevaluate the importance of each feature and the possible coalition thatcontributed to the classification (potential combinations among them todifferentiate obstructive from central hypopnea). To evaluate thecontribution of each features to the prediction, the Lundberg's Shapleyadditive explanation (SHAP) method is adopted which, as such, iswell-known in the art.

These methods allow detecting of, inter alia, obstructive hypopneas andcentral hypopneas.

Example 17

In a further example, reference is made to FIGS. 16 and 17 . Thesefigures show an analysis of mandibular movement data captured by meansof a magnetic sensor. The data analysis as such is similar to dataanalysis of mandibular movement data captured by means of anaccelerometer and/or a gyroscope in addition to a magnetic sensor.

FIG. 16 shows the 18 most important MM signal features derived frommagnetometer measurements, ranked by their global impact on the model'sprediction. The bars indicate the mean SHAP score for each feature,stratified by 3 target labels Central hypopneas (dark grey), Normal(light grey) and Obstructive hypopneas (grey). The SHAP score measuresthe average marginal contribution across all possible coalitions withother features to classify 3 target labels. The higher SHAP score, themore important the contribution that said feature may provide.

FIG. 17 shows the interpretation of an event based on extracted featuresand based on the SHAP score. In particular, FIG. 17 shows the SHAP scorescale and the probability of a target label. FIG. 17 comprises twogeneral regions: region a) and region b). Region a) comprises theextracted features that support the prediction of a target label, andregion b) comprises the extracted features that point away from saidtarget label.

Example 18

In a further example, reference is made to FIG. 18 , which illustratesan exemplary method for determining sleep stages from mandibularmovement data captured by means of gyroscope and an accelerometer. Thesteps discussed below correspond to the reference numbers in FIG. 18 .

In particular, the steps are as follows:

(1) Mandibular movements are recorded during subject sleep using asystem of the present invention comprising a gyroscope and anaccelerometer. The acquired data pack contains 6 channels of raw signalsacquired by said tri-axial accelerometer and gyroscope sensors. The rawdata may further include recordings from other devices suitable fordetermining the for sleep staging, such as EEG, EOG and EMG signals forsleep staging, 6 channels of MM signals acquired by tri-axialaccelerometer and gyroscope sensors

(2) Raw data will pass through a pre-processing and feature generatingmodule, after which it is consecutively segmented into 30 s lengthepochs. The pre-processing consists in producing time series sampled at0.1 Hz and 0.034 Hz (sliding windows of 30 s) from the sleep scoressequences and the time series acquired with the sensor and the PSG. Thispre-processing happens in two steps: the series or sequences aresegmented, then feature extracting functions are applied to each window.

Hand-crafted feature extraction as the input data for machine learningexperiment can be used. For example, a feature generating moduleextracted a set of 1728 features from the 6 channels of MM activitysignal, using a sliding window centered on each 30 seconds epoch. Theextracted features included: signal energy in the low frequency band(0-0.1 Hz), high frequency band (>0.3 Hz) or respiratory frequency band(0.2-0.3 Hz), exponential moving averages with several half-lifeperiods, entropy of the energy in the several frequency bands,statistical features applied on the above features: tendency ofcentrality (mean, median), extreme values (min, max), quartiles,standard deviation, as well the normal standardized value of all abovefeatures.

(3) The extracted features set will be fed to a machine learningclassifier, generating the soft-prediction scores (i.e., theprobability) and binary output for each target label, according to aspecific classification task. The automated sleep staging task wasapproached at three levels of complexity. The task targets are the basic3 sleep stages: Wake, nonREM (including N1, N2, N3) and REM.

The feature selection and hyperparameter tuning were performed withcross-validation, in which the input data were randomly split into foldsat the levels of participants. The final model was trained on the wholetraining set using only the most relevant features and optimizedhyperparameter values. Due to the imbalanced proportion among the targetlabels, the training data was balanced by the Synthetic MinorityOversampling Technique (SMOTE) before each training session.

Machine learning algorithm: Extreme Gradient boosting (XGB) classifieris adopted as the core algorithm for all three classification tasks. TheXGB classifier is optimized during training process by minimizing aregularized objective function that combines a convex loss function(based on the difference between the predicted and target outputs) and apenalty term for model complexity.

Model training: The learning objective is set a multiclassclassification, which aims to classify 3 target labels, depending on thespecific task. The training implied a Dropout-Multiple AdditiveRegression Trees (DART) booster and histogram optimized approximategreedy tree construction algorithm. Logarithmic loss was chosen asevaluation metric (thus optimizing the balanced accuracy among 3 targetclasses). To prevent overfitting, learning rate (eta, or step-sizeshrinkage) parameter is set at 0.01, this will shrink the featureweights to make the boosting process more conservative.

The model's output implied a soft-max function to generate theprobability score for each target label, then the final decision(assigning only one label to each 30 s segment) is achieved by applyingan argmax function on those 3 probability scores.

(4) Depending on the epoch-by-epoch agreement between model's predictionand the reference PSG scoring on the unseen validation dataset, the mostsatisfying solution are adopted for implementation. Further quantitativeevaluations were carried out to verify whether the chosen algorithmcould provide a reliable estimation of the sleep quality scores such asTST, sleep efficiency, REM ratios, and so on. The model selection wasbased on following criteria:

Class-wise agreement evaluation: The normalized confusion matrices allowevaluating the model's class-wise performance for a specific multi-classclassification task. The rows are the truth derived from manual PSGscoring and the columns indicate the results of automated algorithmicscoring. The diagonal cells of the confusion matrix indicate class-wisetrue positive rate.

Precision (or positive predictive value) measures the mode's ability tocorrectly identify the positive cases, defined as True positive/(Truepositive+False positives); Recall (also known as sensitivity, hit rateor true positive rate) indicates the model's utility, defined as thefraction of correct classifications among all targeted instances:Recall=True positive predictions/All positive instances;

F1 score is a combined metric, defined as the harmonic mean of Recalland Precision per class:2*(Precision*Recall)/(Precision+Recall)

F1 score has an intuitive meaning, it indicates how precise the model is(how many epochs it classifies correctly), as well as how robust themodel is (low misclassification rate). Since the real-life data presentthe unbalanced proportions among sleep stages and all labels are equallyimportant, the classifier that gets equally high F1 scores on allclasses is adopted.

Global epoch-by-epoch agreement evaluation metrics: The balancedaccuracy (BAC) measures the mean of true positive and true negativerates among the targeted class. The Cohen's Kappa coefficient measuresthe agreement strength between the model's classification and trueobservations (manual PSG scoring). It could be interpreted as 6 levelsof agreement strength: lower than 0: Poor, 0-2: Slight, 0.2-0.4: Fair,0.41-0.6: Moderate, 0.61-0.8: Substantial, 0.81-1: Almost perfect.

(5) Prediction data from the selected (3 class task) model will passthrough the interpreting module. The first sub-module (sleep scorecomputation) will convert the sequence of predicted sleep stages intoquantitative scores.

Definitions of these quantitative scores are presented in the tablebelow:

Definition Unit Time based indices Total sleep time (TST) Total sleeptime (TST) Hour or minutes Total nonREM sleep time Sum of all epochsclassified as nonREM sleeps Hour or minutes (resolution: 30 s) Total REMsleep time Sum of all epochs classified as REM sleeps Hour or minutes(resolution: 30 s) Ratio based indices NonREM ratio 100 × Total nonREMsleep time/TST Percent (%) REM ratio 100 × Total REM sleep time/TSTPercent (%) Wake ratio 100 × Total Wake time/TST Percent (%) Sleepefficiency ratio 100 × total sleep time/total Percent (%) time in bed(recording) Latency indices Sleep onset latency The elapsed time betweenthe start of minutes recording until the sleep onset REM latency Elapsedtime from the sleep onset minutes to the first REM epoch Occurrencerating Arousal index Total number of all arousal events/TST in h n/h

(6) Hypnogram creation: A customized function converts the sequence ofdiscrete encoded labels (for example: 2=Wake, 1=Rem sleep, 0=nonREMsleep) into a hypnogram. This graph presents the step-lines to representthe discrete sleep stages values as a function of time, which simulatesa conventional hypnogram obtained from manual PSG scoring.

Example 19

Example 19 presents an experimental continuation of Example 18. Inparticular, the method presented in Example 18 was performed on a groupof 96 participants, which were randomly assigned into a training subset(n=68, 70%) and a validation subset (n=28, 30%). Both subsets representa population of healthy adults within an age range of 18 to 53 years.

Mandibular movements were recorded during subject sleep using a systemof the present invention comprising a gyroscope and an accelerometer.The acquired data pack contains 6 channels of raw signals acquired bysaid tri-axial accelerometer and gyroscope sensors. The raw was used todevelop the automated sleep staging model. Additionally, reference datawas recorded using devices suitable for determining the sleep staging,such as EEG, EOG and EMG. The latter data was used to determine theaccuracy of the applied models.

Instead of using deep learning models, a conventional framework wasfollowed, which implies handcrafted features extraction and structureddata driven algorithm. The handcrafted features extraction allows bettercontrol and understanding of input data compared to black-box modelslike convolutional neural network. XGBoost was adopted for theclassification task. This algorithm offers several advantages overclassical methods (LDA, SVM, RF), including high efficiency incomputation and resource, allowing very fast training and executionspeed.

Subject subsets: Polysomnography (PSG) profiles from the group of 96participants indicated a normal sleep activity in both subset groups,with median sleep efficiency of 89.4% and 87.3%. Within each set, thedata structure also presents an imbalance in proportion among 3 sleepstages: except for Wake labels which are regular in most of cases, thenonREM sleeps were predominant over the REM sleeps in both groups (92.3vs 7.7 for trainset and 79.9 vs 20.1 for validation set), suggestingthat a data balancing technique is required during model development,and the performance metrics should be carefully interpreted during modelvalidation.

3 Class scoring: The present model aims to classify Wake (no sleep),nonREM and REM sleeps. The model results in a well-balanced accuracyamong 3 classes (82.9%, 74.9% and 82.5% for wake, nonREM and REM sleeps,respectively). The model also has a substantial agreement strength(Kappa=0.71). It performs best for detecting wake epochs, with F1 scoreof 0.86. Guided by the distribution of Wake, nonREM and REM instances inthe models, identifying the Wake was found to be easier thandistinguishing between nonREM and REM, since the Wake instances werewell clustered and clearly separated from the other instances, whilemost of REM labels were more dispersed and blended into other nonREM orWake points. This pattern suggests that a nonlinear algorithm, such asRandom Forest, XGboost or Deep neural network may be considered forsuccessfully separating 3 classes.

Agreement analysis for the sleep quality indices: The 3 class task sleepstaging algorithm can automatically classify each 30 seconds epoch aswake, nonREM or REM. The outputs were then transformed by a secondalgorithm to provide an estimation of sleep quality indices. Thoseindices could be classified in 3 main categories: a) Time based indices,which measure the cumulated time (in minutes) in sleep (TST) or during aspecific sleep stage, such as Wakefulness, REM or nonREM; b) Ratio basedindices, which are estimated as the percentage of a specific sleep stage(REM, nonREM) over all in-sleep epochs; c) Latency based indices, whichmeasures the elapsed time between the beginning of recording and sleeponset (sleep latency), or between the sleep onset and the first REMepoch (REM latency).

Quantitative scores for the automated sleep staging algorithm weredetermined according to the Table presented in Example 18. Differencesbetween the standard scoring of the PSG profiles and the quantitativescoring of the automated sleep staging algorithm are presented in theTable below:

Parameter Median 95% Cl 97.5% Cl 99% Cl TST (min) −7.148 −18.190 to+2.349 −20.336 to +4.383 −22.758 to +7.430 Total NonREM −26.633 −42.686to −10.616 −45.691 to −6.839 −50.243 to −2.882 sleep time (min) TotalREM sleep +22.560 −3.781 to +51.384 −9.645 to +58.426 −17.495 to +65.436time (min) Total Wake time +11.734 +3.281 to +18.954 +1.517 to +20.423−0.228 to +22.449 (min) Wake index (n/h) +1.478 +1.075 to +1.892 +1.003to +1.989 +0.916 to +2.093 Wake ratio (%) +3.908 +1.346 to +6.100 +0.770to +6.526 +0.078 to +7.039 NonREM ratio (%) −6.423 −10.259 to −2.394−11.006 to −1.595 −12.300 to −0.533 REM ratio (%) +6.469 +0.060 to+13.123 −1.407 to +14.587 −2.824 to +16.334 Sleep efficiency (%) −1.289−2.705 to −0.199 −3.032 to +0.010 −3.402 to +0.232 Sleep latency (min)+1.424 −0.906 to +3.720 −1.569 to +4.166 −2.200 to +4.806 REM latency(min) −17.112 −48.849 to −2.353 −53.831 to +2.375 −60.802 to +7.229

The data indicate that the 3 class based scoring algorithm allowsmeasuring the total sleep time at an acceptable accuracy (mediandifference of only −7.15 minutes, 97.5% CI: −20.34 to +4.38) incomparison to the reference method (manual PSG scoring). The agreementwas also good for determining sleep efficiency (median difference:−1.29%; −3.03 to +0.01).

Conclusions: The feasibility of using mandibular movements recordedduring subject sleep using a system of the present invention comprisinga gyroscope and an accelerometer was explored. The results demonstratethat automated sleep staging detection based on data measured by agyroscope configured for measuring rotational movement offers a betterperformance at all three resolution levels (for 3 class scoring) incomparison to systems of the art comprising an accelerometer only.

TABLE 1 waking or sleeping (N1, N2, N3, or REM) state,positions/movements of the mandible and positions/movements of the headin a state that is assumed to be normal. Example of Positionpreprocessing of the of raw Example of State Position Typical headsignals in the Example of features to (asleep Movements of of the headduring Analysts frequency preprocessing extract or awake) the mandiblemandible movements sleep window domain of raw signals and compare AwakeUnpredictable, duration Instable Presence of Lying or 30 Band-passExponential Normalized average of more than 15 head standing secondsfilter moving average (bigger for waking seconds movements than sleepingstate) Asleep Varies with the Stable No head Lying respiratory frequencymovements N1 Varies with the Stable No head Lying 30 N/A Entropy of theNormalized average sleep respiratory movements seconds frequency of the(bigger for N1 and frequency signal REM than for N2 with variable andN3) peak-to- Amplitude variance peak amplitude, limited duration ofseveral minutes REM Varies with the Stable No head Lying (bigger for N1and sleep respiratory with a movements REM than for N2 frequency, withtendency to and N3) a net variability lowering Frequency variance of thepeak-to-peak (bigger for N1 and amplitude, REM than for N2 non-periodicand N3) N2 Varies with the Stable No head Lying 30 Band-pass ExponentialNormalized median sleep respiratory movements seconds filter movingaverage (lower for N2 and frequency Low-pass filter N3) with a minorNormalized mean variation of (lower for N2 and the peak-to-peak N3)amplitude N3 Varies with the Very No head Lying sleep respiratory Stablemovements frequency with minor long-term (more than 10 minutes)variation of the peak-to- peak amplitude

TABLE 2 cerebral activations - cortical and sub-cortical activationsExample of Typical preprocessing head of raw signals Typical Typicalposition in the Example of Example of Cerebral Typical mandible mandiblehead during Exemplary frequency preprocessing features to activationsmovements position movements sleep analysis domain of raw signalsextract Cortical Abrupt and high- Unstable With or Lying, 10 Low-passfilter N/A Amplitude amplitude closing or between without typicallyseconds and duration opening duration two position with between 3 and 15extremes changes of position seconds the head changes of the headSub-cortical Break in the Stable With or Lying 10 Band-pass ExponentialAmplitude oscillation/respiratory frequency without seconds filtermoving and duration Mandible movement position average of smallamplitude or changes of of moderate the head amplitude Short duration

TABLE 3 typical behavior of cerebral contral far the detection ofrespiratory and non-respiratory motor events Amplitude Frequency of theCentrality of of the Variance of signal Example the signal signal thatthe signal that of that provides provides that provides providespreprosessing information information information information of raw onthe state on the on the state on the state Examples of signals Exampleof of the state of the of the of the relevant in the preprocessingExample of cerebral cerebral cerebral cerebral analysis frequency of rawfeatures Events control control control control windows domain signalsto extract Obstructive On the decline Significant, Non-cyclicalRespiratory 10 Band Exponential Centrality (e.g. apnea- (other mayduring the seconds pass filter moving average, mean, hypopnea behavioursstrongly event but the average modes) may be increase event may beExtremities (e.g. observed as periodical maximum, centiles) well)Distribution (e.g, shape) Duration Variance Respiratory Unchanged orSignificant, None (stable), Respiratory Effort slightly down mayslightly or week, or Related increase increasing Arousal (RERA) CentralOn the Very weak None (stable), Respiratory apnea- decline or zero orweak, hypopnea (other sometimes behaviors may be observed as well)periodical Trend (increase- decrease or not) Bruxism Stable VeryNon-cyclical Non- 30 Band Entropy of Centrality (e.g. significantrespiratory seconds pass filter the frequency average, mean, (typically1of the signal modes) Hz) Extremities (e.g. maximum, centiles) No eventStable Very weak None Respiratory 30 N/A N/A N/A or zero (stable)seconds

What is claimed is:
 1. A method for determining a sleep event of asubject, the method comprising: receiving first data generated by agyroscope of a sensing unit externally mounted onto a mandible of thesubject, the first data indicative of rotational movement of themandible of the subject; receiving second data generated by anaccelerometer of the sensing unit externally mounted onto the mandibleof the subject, the second data indicative of acceleration of themandible of the subject; determining using at least one machine learningalgorithm that the first data and second data correspond to a firstmandible movement class of a plurality of mandible movement classesbased on the first data corresponding to a first rotational movementvalue associated with the first mandible movement class and the seconddata corresponding to a first acceleration value associated with thefirst mandible movement class; determining the first data and seconddata correspond to a sleep event associated with the first mandiblemovement class, the first data indicative of movement of the mandiblecaused by one or more antagonist or agonist muscles connected to themandible of the subject and the second data indicative of movement ofthe mandible caused by a tracheal tug of the subject; generatingdiagnostic information for diagnosing a sleep disorder associated withthe sleep event based on the first data and the second data; andgenerating a report comprising the diagnostic information for diagnosingthe sleep disorder associated with the sleep event.
 2. The method ofclaim 1, wherein each mandible movement class of the plurality ofmandible movement classes corresponds to at least one rotationalmovement value and at least one acceleration value.
 3. The method ofclaim 1, wherein the rotational movement of the mandible of the subjectis indicative of one or more rate, rate change, frequency, or amplitudeof rotations of the mandible of the subject.
 4. The method of claim 1,wherein receiving the first data and receiving the second data compriseswirelessly receiving the first data and the second data from a telephonein wireless communication with a data link of the sensing unit.
 5. Themethod of claim 1, further comprising receiving third data generated bya magnetometer positioned on the mandible of the subject, the third dataindicative of magnetic field data.
 6. The method of claim 5, furthercomprising determining using the at least one machine learning algorithmthat the third data corresponds to the first mandible movement class ofthe plurality of mandible movement classes based on the third datacorresponding to a first magnetic field data value associated with thefirst mandible movement class.
 7. The method of claim 1, wherein thesleep event is bruxism.
 8. The method of claim 1, wherein the sleepevent is obstructive apnea, obstructive hypopnea, respiratory effortrelated arousal, central apnea, or central hypopnea.
 9. The method ofclaim 1, further comprising: receiving third data generated by thegyroscope and indicative of rotational movement of the mandible of thesubject; receiving fourth data generated by the accelerometer andindicative of acceleration of the mandible of the subject; anddetermining using the at least one machine learning algorithm that thethird data and fourth data corresponds to a second mandible movementclass of the plurality of mandible movement classes based on the thirddata corresponding to a second rotational movement value associated withthe second mandible movement class and the fourth data corresponding toa second acceleration value associated with the second mandible movementclass.
 10. The method of claim 1, wherein the first mandible movementclass is indicative of a N1, N2, N3, or rapid eye movement (REM) sleepstate.
 11. A system comprising: a sensing unit comprising a gyroscope,an accelerometer, and a data link in communication with the gyroscopeand the accelerometer, the sensing unit adapted to be externally mountedonto a mandible of the subject; memory configured to storecomputer-executable instructions, and at least one computer processorconfigured to access memory and execute the computer-executableinstructions to: receive first data from the data link, the first datagenerated by the gyroscope and indicative of rotational movement of themandible of the subject; receive second data from the data link, thesecond data generated by the accelerometer and indicative ofacceleration of the mandible of the subject; determine that the firstdata and second data correspond to a first mandible movement class of aplurality of mandible movement classes based on the first datacorresponding to a first rotational movement value associated with thefirst mandible movement class and the second data corresponding to afirst acceleration value associated with the first mandible movementclass; determine the first data and second data correspond to a sleepevent associated with the first mandible movement class, the first dataindicative of movement of the mandible caused by one or more antagonistor agonist muscles connected to the mandible of the subject and thesecond data indicative of movement of the mandible caused by a trachealtug of the subject; generate diagnostic information for diagnosing asleep disorder associated with the sleep event based on the first dataand the second data; and generate a report comprising the diagnosticinformation for diagnosing the sleep disorder associated with the sleepevent.
 12. The system of claim 11, wherein at least one machine learningalgorithm is used to determine that the first data and second datacorresponds to the first mandible movement class of a plurality ofmandible movement classes.
 13. The system of claim 11, wherein eachmandible movement class of the plurality of mandible movement classescorresponds to at least one rotational movement value and at least oneacceleration value.
 14. The system of claim 11, wherein the rotationalmovement of the mandible of the subject is indicative of one or morerate, rate change, frequency, or amplitude of rotations of the mandibleof the subject.
 15. The system of claim 11, wherein the data linkcomprises wireless communication circuitry configured to wirelesslytransmit the first data and the second data to a telephone.
 16. Thesystem of claim 11, wherein the sensing unit adapted to be externallymounted on the mandible further comprises a magnetometer incommunication with the data link; and wherein the at least one computerprocessor is further configured to access memory and execute thecomputer-executable instructions to receive third data transmitted bythe data link, the third data generated by the magnetometer andindicative of magnetic field data.
 17. The system of claim 16, andwherein the at least one computer processor is further configured toaccess memory and execute the computer-executable instructions todetermine that that the third data corresponds to the first mandiblemovement class of the plurality of mandible movement classes based onthe third data corresponding to a first magnetic field data valueassociated with the first mandible movement class.
 18. The system ofclaim 11, wherein the sleep event is bruxism.
 19. The system of claim11, wherein the sleep event is obstructive apnea, obstructive hypopnea,respiratory effort related arousal, central apnea, or central hynopnea.20. The system of claim 11, wherein the first mandible movement class isindicative of a N1, N2, N3, or rapid eye movement (REM) sleep state. 21.A non-transitory computer-readable memory medium configured to storeinstructions thereon that when loaded by at least one processor causethe at least one processor to: receive first data generated by agyroscope of a sensing unit externally mounted onto a mandible of thesubject, the first data indicative of rotational movement of themandible of the subject; receive second data generated by anaccelerometer of the sensing unit externally mounted onto the mandibleof the subject, the second data indicative of acceleration of themandible of the subject; determine using at least one machine learningalgorithm that the first data and second data corresponds to a firstmandible movement class of a plurality of mandible movement classesbased on the first data corresponding to a first rotational movementvalue associated with the first mandible movement class and the seconddata corresponding to a first acceleration value associated with thefirst mandible movement class; determine the first data and second datacorrespond to a sleep event associated with the first mandible movementclass, the first data indicative of movement of the mandible caused byone or more antagonist or agonist muscles connected to the mandible ofthe subject and the second data indicative of movement of the mandiblecaused by a tracheal tug of the subject; generate diagnostic informationfor diagnosing a sleep disorder associated with the sleep event based onthe first data and the second data; and generate a report comprising thediagnostic information for diagnosing the sleep disorder associated withthe sleep event.
 22. The non-transitory computer-readable memory mediumof claim 21, wherein each mandible movement class of the plurality ofmandible movement classes corresponds to at least one rotationalmovement value and at least one acceleration value.
 23. Thenon-transitory computer-readable memory medium of claim 21, wherein thesleep event is obstructive apnea, obstructive hypopnea, respiratoryeffort related arousal, central apnea, or central hynopnea.
 24. Thenon-transitory computer-readable memory medium of claim 21, wherein theinstructions further cause the processor to: receive third datagenerated by the gyroscope and indicative of rotational movement of themandible of the subject; receive fourth data generated by theaccelerometer and indicative of acceleration of the mandible of thesubject; and determine using the at least one machine learning algorithmthat the third data and fourth data corresponds to a second mandiblemovement class of the plurality of mandible movement classes based onthe third data corresponding to a second rotational movement valueassociated with the second mandible movement class and the fourth datacorresponding to a second acceleration value associated with the secondmandible movement class.
 25. The non-transitory computer-readable memorymedium of claim 21, wherein the first mandible movement class isindicative of a N1, N2, N3, or rapid eye movement (REM) sleep state. 26.The system of claim 11, wherein the sensing unit adapted to beexternally mounted on the mandible further comprises aphotoplethysmogram (PPG) configured to generate PPG data to be receivedand processed by the at least one computer processor.
 27. The system ofclaim 11, wherein the sensing unit adapted to be externally mounted onthe mandible further comprises a thermistor configured to generatethermistor data to be received and processed by the at least onecomputer processor.
 28. The system of claim 11, wherein the sensing unitadapted to be externally mounted on the mandible further comprises aphotoplethysmogram (PPG) and a thermistor, the PPG configured togenerate PPG data to be received and processed by the at least onecomputer processor and the thermistor configured to generate thermistordata to be received and processed by the at least one computerprocessor.
 29. The system of claim 11, wherein the sensing unit adaptedto be externally mounted on the mandible further comprises an audiosensor configured to generate audio data to be received and processed bythe at least one computer processor.
 30. The system of claim 11, whereinthe sensing unit adapted to be externally mounted on the mandiblefurther comprises a photoplethysmogram (PPG), a thermistor, and an audiosensor, the PPG configured to generate PPG data to be received andprocessed by the at least one computer processor, the thermistorconfigured to generate thermistor data to be received and processed bythe at least one computer processor, and the audio sensor configured togenerate audio data to be received and processed by the at least onecomputer processor.